图像特征检测(Image Feature Detection)

ゝ一纸荒年。 2022-05-29 02:49 223阅读 0赞

[http://www.cnblogs.com/xrwang/archive/2010/03/03/ImageFeatureDetection.html][http_www.cnblogs.com_xrwang_archive_2010_03_03_ImageFeatureDetection.html]

作者：王先荣  
**前言**  
图像特征提取是计算机视觉和图像处理中的一个概念。它指的是使用计算机提取图像信息，决定每个图像的点是否属于一个图像特征。本文主要探讨如何提取图像中的“角点”这一特征，及其相关的内容。而诸如直方图、边缘、区域等内容在前文中有所提及，请查看相关文章。OpenCv（EmguCv）中实现了多种角点特征的提取方法，包括：Harris角点、ShiTomasi角点、亚像素级角点、SURF角点、Star关键点、FAST关键点、Lepetit关键点等等，本文将逐一介绍如何检测这些角点。在此之前将会先介绍跟角点检测密切相关的一些变换，包括Sobel算子、拉普拉斯算子、Canny算子、霍夫变换。另外，还会介绍一种广泛使用而OpenCv中并未实现的SIFT角点检测，以及最近在OpenCv中实现的MSER区域检测。所要讲述的内容会很多，我这里尽量写一些需要注意的地方及实现代码，而参考手册及书本中有的内容将一笔带过或者不会提及。

**Sobel算子**  
Sobel算子用多项式计算来拟合导数计算，可以用OpenCv中的cvSobel函数或者EmguCv中的 Image<TColor,TDepth>.Sobel方法来进行计算。需要注意的是，xorder和yorder中必须且只能有一个为非零值，即只能计算x方向或者y反向的导数；如果将方形滤波器的宽度设置为特殊值CV\_SCHARR（-1），将使用Scharr滤波器代替Sobel滤波器。  
使用Sobel滤波器的示例代码如下：

![ExpandedBlockStart.gif][] Sobel算子

![复制代码][copycode.gif]

//
         
          Sobel算子
         
          
    
         
                  
         
          private
         
           
         
          string
         
           SobelFeatureDetect()
            {
                
         
          //
         
          获取参数
         
          
    
         
                      
         
          int
         
           xOrder 
         
          =
         
           
         
          int
         
          .Parse((
         
          string
         
          )cmbSobelXOrder.SelectedItem);
                
         
          int
         
           yOrder 
         
          =
         
           
         
          int
         
          .Parse((
         
          string
         
          )cmbSobelYOrder.SelectedItem);
                
         
          int
         
           apertureSize 
         
          =
         
           
         
          int
         
          .Parse((
         
          string
         
          )cmbSobelApertureSize.SelectedItem);
                
         
          if
         
           ((xOrder 
         
          ==
         
           
         
          0
         
           
         
          &&
         
           yOrder 
         
          ==
         
           
         
          0
         
          ) 
         
          ||
         
           (xOrder 
         
          !=
         
           
         
          0
         
           
         
          &&
         
           yOrder 
         
          !=
         
           
         
          0
         
          ))
                    
         
          return
         
           
         
          "
         
          Sobel算子，参数错误：xOrder和yOrder中必须且只能有一个非零。\r\n
         
          "
         
          ;
                
         
          //
         
          计算
         
          
    
         
                      Stopwatch sw 
         
          =
         
           
         
          new
         
           Stopwatch();
                sw.Start();
                Image
         
          <
         
          Gray, Single
         
          >
         
           imageDest 
         
          =
         
           imageSourceGrayscale.Sobel(xOrder, yOrder, apertureSize);
                sw.Stop();
                
         
          //
         
          显示
         
          
    
         
                      pbResult.Image 
         
          =
         
           imageDest.Bitmap;
                
         
          //
         
          释放资源
         
          
    
         
                      imageDest.Dispose();
                
         
          //
         
          返回
         
          
    
         
                      
         
          return
         
           
         
          string
         
          .Format(
         
          "
         
          ·Sobel算子，用时{0:F05}毫秒，参数（x方向求导阶数：{1}，y方向求导阶数：{2}，方形滤波器宽度：{3}）\r\n
         
          "
         
          , sw.Elapsed.TotalMilliseconds, xOrder, yOrder, apertureSize);
            }

![复制代码][copycode.gif]

**拉普拉斯算子**  
拉普拉斯算子可以用作边缘检测；可以用OpenCv中的cvLaplace函数或者EmguCv中的 Image<TColor,TDepth>.Laplace方法来进行拉普拉斯变换。需要注意的是：OpenCv的文档有点小错误，apertureSize参数值不能为CV\_SCHARR（-1）。  
使用拉普拉斯变换的示例代码如下：

![ExpandedBlockStart.gif][] 拉普拉斯算子

![复制代码][copycode.gif]

//
         
          拉普拉斯变换
         
          
    
         
                  
         
          private
         
           
         
          string
         
           LaplaceFeatureDetect()
            {
                
         
          //
         
          获取参数
         
          
    
         
                      
         
          int
         
           apertureSize 
         
          =
         
           
         
          int
         
          .Parse((
         
          string
         
          )cmbLaplaceApertureSize.SelectedItem);
                
         
          //
         
          计算
         
          
    
         
                      Stopwatch sw 
         
          =
         
           
         
          new
         
           Stopwatch();
                sw.Start();
                Image
         
          <
         
          Gray, Single
         
          >
         
           imageDest 
         
          =
         
           imageSourceGrayscale.Laplace(apertureSize);
                sw.Stop();
                
         
          //
         
          显示
         
          
    
         
                      pbResult.Image 
         
          =
         
           imageDest.Bitmap;
                
         
          //
         
          释放资源
         
          
    
         
                      imageDest.Dispose();
                
         
          //
         
          返回
         
          
    
         
                      
         
          return
         
           
         
          string
         
          .Format(
         
          "
         
          ·拉普拉斯变换，用时{0:F05}毫秒，参数（方形滤波器宽度：{1}）\r\n
         
          "
         
          , sw.Elapsed.TotalMilliseconds, apertureSize);
            }

![复制代码][copycode.gif]

**Canny算子**  
 Canny算子也可以用作边缘检测；可以用OpenCv中的cvCanny函数或者EmguCv中的 Image<TColor,TDepth>.Canny方法来进行Canny边缘检测。所不同的是，Image<TColor,TDepth>.Canny方法可以用于检测彩色图像的边缘，但是它只能使用apertureSize参数的默认值3；  
而cvCanny只能处理灰度图像，不过可以自定义apertureSize。cvCanny和Canny的方法参数名有点点不同，下面是参数对照表。  
Image<TColor,TDepth>.Canny CvInvoke.cvCanny  
thresh lowThresh  
threshLinking highThresh  
3 apertureSize  
值得注意的是，apertureSize只能取3，5或者7，这可以在cvcanny.cpp第87行看到：

aperture_size 
        
         &=
        
          INT_MAX;
        
        
         if
        
         ( (aperture_size 
        
         &
        
          
        
         1
        
         ) 
        
         ==
        
          
        
         0
        
          
        
         ||
        
          aperture_size 
        
         <
        
          
        
         3
        
          
        
         ||
        
          aperture_size 
        
         >
        
          
        
         7
        
          )
            CV_ERROR( CV_StsBadFlag, 
        
         ""
        
          );

使用Canny算子的示例代码如下：

![ExpandedBlockStart.gif][] Canny算子

![复制代码][copycode.gif]

//
         
          Canny算子
         
          
    
         
                  
         
          private
         
           
         
          string
         
           CannyFeatureDetect()
            {
                
         
          //
         
          获取参数
         
          
    
         
                      
         
          double
         
           lowThresh 
         
          =
         
           
         
          double
         
          .Parse(txtCannyLowThresh.Text);
                
         
          double
         
           highThresh 
         
          =
         
           
         
          double
         
          .Parse(txtCannyHighThresh.Text);
                
         
          int
         
           apertureSize 
         
          =
         
           
         
          int
         
          .Parse((
         
          string
         
          )cmbCannyApertureSize.SelectedItem);
                
         
          //
         
          计算
         
          
    
         
                      Stopwatch sw 
         
          =
         
           
         
          new
         
           Stopwatch();
                sw.Start();
                Image
         
          <
         
          Gray, Byte
         
          >
         
           imageDest 
         
          =
         
           
         
          null
         
          ;
                Image
         
          <
         
          Bgr, Byte
         
          >
         
           imageDest2 
         
          =
         
           
         
          null
         
          ;
                
         
          if
         
           (rbCannyUseCvCanny.Checked)
                {
                    imageDest 
         
          =
         
           
         
          new
         
           Image
         
          <
         
          Gray, 
         
          byte
         
          >
         
          (imageSourceGrayscale.Size);
                    CvInvoke.cvCanny(imageSourceGrayscale.Ptr, imageDest.Ptr, lowThresh, highThresh, apertureSize);
                }
                
         
          else
         
          
                    imageDest2 
         
          =
         
           imageSource.Canny(
         
          new
         
           Bgr(lowThresh, lowThresh, lowThresh), 
         
          new
         
           Bgr(highThresh, highThresh, highThresh));
                sw.Stop();
                
         
          //
         
          显示
         
          
    
         
                      pbResult.Image 
         
          =
         
           rbCannyUseCvCanny.Checked 
         
          ?
         
           imageDest.Bitmap : imageDest2.Bitmap;
                
         
          //
         
          释放资源
         
          
    
         
                      
         
          if
         
           (imageDest 
         
          !=
         
           
         
          null
         
          )
                    imageDest.Dispose();
                
         
          if
         
           (imageDest2 
         
          !=
         
           
         
          null
         
          )
                    imageDest2.Dispose();
                
         
          //
         
          返回
         
          
    
         
                      
         
          return
         
           
         
          string
         
          .Format(
         
          "
         
          ·Canny算子，用时{0:F05}毫秒，参数（方式：{1}，阀值下限：{2}，阀值上限：{3}，方形滤波器宽度：{4}）\r\n
         
          "
         
          , sw.Elapsed.TotalMilliseconds, rbCannyUseCvCanny.Checked 
         
          ?
         
           
         
          "
         
          cvCanny
         
          "
         
           : 
         
          "
         
          Image<TColor, TDepth>.Canny
         
          "
         
          , lowThresh, highThresh, apertureSize);
            }

![复制代码][copycode.gif]

另外，在[http://www.china-vision.net/blog/user2/15975/archives/2007/804.html][http_www.china-vision.net_blog_user2_15975_archives_2007_804.html]有一种自动获取Canny算子高低阀值的方法，作者提供了用C语言实现的代码。我将其改写成了C\#版本，代码如下：

![ExpandedBlockStart.gif][] 计算图像的自适应Canny算子阀值

![复制代码][copycode.gif]

///
         
           
         
          <summary>
         
          
            
         
          ///
         
           计算图像的自适应Canny算子阀值
            
         
          ///
         
           
         
          </summary>
         
          
            
         
          ///
         
           
         
          <param name="imageSrc">
         
          源图像，只能是256级灰度图像
         
          </param>
         
          
            
         
          ///
         
           
         
          <param name="apertureSize">
         
          方形滤波器的宽度
         
          </param>
         
          
            
         
          ///
         
           
         
          <param name="lowThresh">
         
          阀值下限
         
          </param>
         
          
            
         
          ///
         
           
         
          <param name="highThresh">
         
          阀值上限
         
          </param>
         
          
    
         
                  
         
          unsafe
         
           
         
          void
         
           AdaptiveFindCannyThreshold(Image
         
          <
         
          Gray, Byte
         
          >
         
           imageSrc, 
         
          int
         
           apertureSize, 
         
          out
         
           
         
          double
         
           lowThresh, 
         
          out
         
           
         
          double
         
           highThresh)
            {
                
         
          //
         
          计算源图像x方向和y方向的1阶Sobel算子
         
          
    
         
                      Size size 
         
          =
         
           imageSrc.Size;
                Image
         
          <
         
          Gray, Int16
         
          >
         
           imageDx 
         
          =
         
           
         
          new
         
           Image
         
          <
         
          Gray, 
         
          short
         
          >
         
          (size);
                Image
         
          <
         
          Gray, Int16
         
          >
         
           imageDy 
         
          =
         
           
         
          new
         
           Image
         
          <
         
          Gray, 
         
          short
         
          >
         
          (size);
                CvInvoke.cvSobel(imageSrc.Ptr, imageDx.Ptr, 
         
          1
         
          , 
         
          0
         
          , apertureSize);
                CvInvoke.cvSobel(imageSrc.Ptr, imageDy.Ptr, 
         
          0
         
          , 
         
          1
         
          , apertureSize);
                Image
         
          <
         
          Gray, Single
         
          >
         
           image 
         
          =
         
           
         
          new
         
           Image
         
          <
         
          Gray, 
         
          float
         
          >
         
          (size);
                
         
          int
         
           i, j;
                DenseHistogram hist 
         
          =
         
           
         
          null
         
          ;
                
         
          int
         
           hist_size 
         
          =
         
           
         
          255
         
          ;
                
         
          float
         
          [] range_0 
         
          =
         
           
         
          new
         
           
         
          float
         
          [] { 
         
          0
         
          , 
         
          256
         
           };
                
         
          double
         
           PercentOfPixelsNotEdges 
         
          =
         
           
         
          0.7
         
          ;
                
         
          //
         
          计算边缘的强度，并保存于图像中
         
          
    
         
                      
         
          float
         
           maxv 
         
          =
         
           
         
          0
         
          ;
                
         
          float
         
           temp;
                
         
          byte
         
          *
         
           imageDataDx 
         
          =
         
           (
         
          byte
         
          *
         
          )imageDx.MIplImage.imageData.ToPointer();
                
         
          byte
         
          *
         
           imageDataDy 
         
          =
         
           (
         
          byte
         
          *
         
          )imageDy.MIplImage.imageData.ToPointer();
                
         
          byte
         
          *
         
           imageData 
         
          =
         
           (
         
          byte
         
          *
         
          )image.MIplImage.imageData.ToPointer();
                
         
          int
         
           widthStepDx 
         
          =
         
           imageDx.MIplImage.widthStep;
                
         
          int
         
           widthStepDy 
         
          =
         
           widthStepDx;
                
         
          int
         
           widthStep 
         
          =
         
           image.MIplImage.widthStep;
                
         
          for
         
           (i 
         
          =
         
           
         
          0
         
          ; i 
         
          <
         
           size.Height; i
         
          ++
         
          )
                {
                    
         
          short
         
          *
         
           _dx 
         
          =
         
           (
         
          short
         
          *
         
          )(imageDataDx 
         
          +
         
           widthStepDx 
         
          *
         
           i);
                    
         
          short
         
          *
         
           _dy 
         
          =
         
           (
         
          short
         
          *
         
          )(imageDataDy 
         
          +
         
           widthStepDy 
         
          *
         
           i);
                    
         
          float
         
          *
         
           _image 
         
          =
         
           (
         
          float
         
          *
         
          )(imageData 
         
          +
         
           widthStep 
         
          *
         
           i);
                    
         
          for
         
           (j 
         
          =
         
           
         
          0
         
          ; j 
         
          <
         
           size.Width; j
         
          ++
         
          )
                    {
                        temp 
         
          =
         
           (
         
          float
         
          )(Math.Abs(
         
          *
         
          (_dx 
         
          +
         
           j)) 
         
          +
         
           Math.Abs(
         
          *
         
          (_dy 
         
          +
         
           j)));
                        
         
          *
         
          (_image 
         
          +
         
           j) 
         
          =
         
           temp;
                        
         
          if
         
           (maxv 
         
          <
         
           temp)
                            maxv 
         
          =
         
           temp;
                    }
                }
                
         
          //
         
          计算直方图
         
          
    
         
                      range_0[
         
          1
         
          ] 
         
          =
         
           maxv;
                hist_size 
         
          =
         
           hist_size 
         
          >
         
           maxv 
         
          ?
         
           (
         
          int
         
          )maxv : hist_size;
                hist 
         
          =
         
           
         
          new
         
           DenseHistogram(hist_size, 
         
          new
         
           RangeF(range_0[
         
          0
         
          ], range_0[
         
          1
         
          ]));
                hist.Calculate
         
          <
         
          Single
         
          >
         
          (
         
          new
         
           Image
         
          <
         
          Gray, Single
         
          >
         
          [] { image }, 
         
          false
         
          , 
         
          null
         
          );
                
         
          int
         
           total 
         
          =
         
           (
         
          int
         
          )(size.Height 
         
          *
         
           size.Width 
         
          *
         
           PercentOfPixelsNotEdges);
                
         
          double
         
           sum 
         
          =
         
           
         
          0
         
          ;
                
         
          int
         
           icount 
         
          =
         
           hist.BinDimension[
         
          0
         
          ].Size;
                
         
          for
         
           (i 
         
          =
         
           
         
          0
         
          ; i 
         
          <
         
           icount; i
         
          ++
         
          )
                {
                    sum 
         
          +=
         
           hist[i];
                    
         
          if
         
           (sum 
         
          >
         
           total)
                        
         
          break
         
          ;
                }
                
         
          //
         
          计算阀值
         
          
    
         
                      highThresh 
         
          =
         
           (i 
         
          +
         
           
         
          1
         
          ) 
         
          *
         
           maxv 
         
          /
         
           hist_size;
                lowThresh 
         
          =
         
           highThresh 
         
          *
         
           
         
          0.4
         
          ;
                
         
          //
         
          释放资源
         
          
    
         
                      imageDx.Dispose();
                imageDy.Dispose(); image.Dispose();
                hist.Dispose();
            }

![复制代码][copycode.gif]

**霍夫变换**  
霍夫变换是一种在图像中寻找直线、圆及其他简单形状的方法，在OpenCv中实现了霍夫线变换和霍夫圆变换。值得注意的地方有以下几点：（1）HoughLines2需要先计算Canny边缘，然后再检测直线；（2）HoughLines2计算结果的获取随获取方式的不同而不同；（3）HoughCircles检测结果似乎不正确。  
使用霍夫变换的示例代码如下所示：

![ExpandedBlockStart.gif][] 霍夫变换

![复制代码][copycode.gif]

//
         
          霍夫线变换
         
          
    
         
                  
         
          private
         
           
         
          string
         
           HoughLinesFeatureDetect()
            {
                
         
          //
         
          获取参数
         
          
    
         
                      HOUGH_TYPE method 
         
          =
         
           rbHoughLinesSHT.Checked 
         
          ?
         
           HOUGH_TYPE.CV_HOUGH_STANDARD : (rbHoughLinesPPHT.Checked 
         
          ?
         
           HOUGH_TYPE.CV_HOUGH_PROBABILISTIC : HOUGH_TYPE.CV_HOUGH_MULTI_SCALE);
                
         
          double
         
           rho 
         
          =
         
           
         
          double
         
          .Parse(txtHoughLinesRho.Text);
                
         
          double
         
           theta 
         
          =
         
           
         
          double
         
          .Parse(txtHoughLinesTheta.Text);
                
         
          int
         
           threshold 
         
          =
         
           
         
          int
         
          .Parse(txtHoughLinesThreshold.Text);
                
         
          double
         
           param1 
         
          =
         
           
         
          double
         
          .Parse(txtHoughLinesParam1.Text);
                
         
          double
         
           param2 
         
          =
         
           
         
          double
         
          .Parse(txtHoughLinesParam2.Text);
                MemStorage storage 
         
          =
         
           
         
          new
         
           MemStorage();
                
         
          int
         
           linesCount 
         
          =
         
           
         
          0
         
          ;
                StringBuilder sbResult 
         
          =
         
           
         
          new
         
           StringBuilder();
                
         
          //
         
          计算，先运行Canny边缘检测（参数来自Canny算子属性页），然后再用计算霍夫线变换
         
          
    
         
                      
         
          double
         
           lowThresh 
         
          =
         
           
         
          double
         
          .Parse(txtCannyLowThresh.Text);
                
         
          double
         
           highThresh 
         
          =
         
           
         
          double
         
          .Parse(txtCannyHighThresh.Text);
                
         
          int
         
           apertureSize 
         
          =
         
           
         
          int
         
          .Parse((
         
          string
         
          )cmbCannyApertureSize.SelectedItem);
                Image
         
          <
         
          Gray, Byte
         
          >
         
           imageCanny 
         
          =
         
           
         
          new
         
           Image
         
          <
         
          Gray, 
         
          byte
         
          >
         
          (imageSourceGrayscale.Size);
                CvInvoke.cvCanny(imageSourceGrayscale.Ptr, imageCanny.Ptr, lowThresh, highThresh, apertureSize);
                Stopwatch sw 
         
          =
         
           
         
          new
         
           Stopwatch();
                sw.Start();
                IntPtr ptrLines 
         
          =
         
           CvInvoke.cvHoughLines2(imageCanny.Ptr, storage.Ptr, method, rho, theta, threshold, param1, param2);
                Seq
         
          <
         
          LineSegment2D
         
          >
         
           linesSeq 
         
          =
         
           
         
          null
         
          ;
                Seq
         
          <
         
          PointF
         
          >
         
           linesSeq2 
         
          =
         
           
         
          null
         
          ;
                
         
          if
         
           (method 
         
          ==
         
           HOUGH_TYPE.CV_HOUGH_PROBABILISTIC)
                    linesSeq 
         
          =
         
           
         
          new
         
           Seq
         
          <
         
          LineSegment2D
         
          >
         
          (ptrLines, storage);
                
         
          else
         
          
                    linesSeq2 
         
          =
         
           
         
          new
         
           Seq
         
          <
         
          PointF
         
          >
         
          (ptrLines, storage);
                sw.Stop();
                
         
          //
         
          显示
         
          
    
         
                      Image
         
          <
         
          Bgr, Byte
         
          >
         
           imageResult 
         
          =
         
           imageSourceGrayscale.Convert
         
          <
         
          Bgr, Byte
         
          >
         
          ();
                
         
          if
         
           (linesSeq 
         
          !=
         
           
         
          null
         
          )
                {
                    linesCount 
         
          =
         
           linesSeq.Total;
                    
         
          foreach
         
           (LineSegment2D line 
         
          in
         
           linesSeq)
                    {
                        imageResult.Draw(line, 
         
          new
         
           Bgr(255d, 0d, 0d), 
         
          4
         
          );
                        sbResult.AppendFormat(
         
          "
         
          {0}-{1}，
         
          "
         
          , line.P1, line.P2);
                    }
                }
                
         
          else
         
          
                {
                    linesCount 
         
          =
         
           linesSeq2.Total;
                    
         
          foreach
         
           (PointF line 
         
          in
         
           linesSeq2)
                    {
                        
         
          float
         
           r 
         
          =
         
           line.X;
                        
         
          float
         
           t 
         
          =
         
           line.Y;
                        
         
          double
         
           a 
         
          =
         
           Math.Cos(t), b 
         
          =
         
           Math.Sin(t);
                        
         
          double
         
           x0 
         
          =
         
           a 
         
          *
         
           r, y0 
         
          =
         
           b 
         
          *
         
           r;
                        
         
          int
         
           x1 
         
          =
         
           (
         
          int
         
          )(x0 
         
          +
         
           
         
          1000
         
           
         
          *
         
           (
         
          -
         
          b));
                        
         
          int
         
           y1 
         
          =
         
           (
         
          int
         
          )(y0 
         
          +
         
           
         
          1000
         
           
         
          *
         
           (a));
                        
         
          int
         
           x2 
         
          =
         
           (
         
          int
         
          )(x0 
         
          -
         
           
         
          1000
         
           
         
          *
         
           (
         
          -
         
          b));
                        
         
          int
         
           y2 
         
          =
         
           (
         
          int
         
          )(y0 
         
          -
         
           
         
          1000
         
           
         
          *
         
           (a));
                        Point pt1 
         
          =
         
           
         
          new
         
           Point(x1, y1);
                        Point pt2 
         
          =
         
           
         
          new
         
           Point(x2, y2);
                        imageResult.Draw(
         
          new
         
           LineSegment2D(pt1, pt2), 
         
          new
         
           Bgr(255d, 0d, 0d), 
         
          4
         
          );
                        sbResult.AppendFormat(
         
          "
         
          {0}-{1}，
         
          "
         
          , pt1, pt2);
                    }
                }
                pbResult.Image 
         
          =
         
           imageResult.Bitmap;
                
         
          //
         
          释放资源
         
          
    
         
                      imageCanny.Dispose();
                imageResult.Dispose();
                storage.Dispose();
                
         
          //
         
          返回
         
          
    
         
                      
         
          return
         
           
         
          string
         
          .Format(
         
          "
         
          ·霍夫线变换，用时{0:F05}毫秒，参数（变换方式：{1}，距离精度：{2}，弧度精度：{3}，阀值：{4}，参数1：{5}，参数2：{6}），找到{7}条直线\r\n{8}
         
          "
         
          ,
                    sw.Elapsed.TotalMilliseconds, method.ToString(
         
          "
         
          G
         
          "
         
          ), rho, theta, threshold, param1, param2, linesCount, linesCount 
         
          !=
         
           
         
          0
         
           
         
          ?
         
           (sbResult.ToString() 
         
          +
         
           
         
          "
         
          \r\n
         
          "
         
          ) : 
         
          ""
         
          );
            }
    
            
         
          //
         
          霍夫圆变换
         
          
    
         
                  
         
          private
         
           
         
          string
         
           HoughCirclesFeatureDetect()
            {
                
         
          //
         
          获取参数
         
          
    
         
                      
         
          double
         
           dp 
         
          =
         
           
         
          double
         
          .Parse(txtHoughCirclesDp.Text);
                
         
          double
         
           minDist 
         
          =
         
           
         
          double
         
          .Parse(txtHoughCirclesMinDist.Text);
                
         
          double
         
           param1 
         
          =
         
           
         
          double
         
          .Parse(txtHoughCirclesParam1.Text);
                
         
          double
         
           param2 
         
          =
         
           
         
          double
         
          .Parse(txtHoughCirclesParam2.Text);
                
         
          int
         
           minRadius 
         
          =
         
           
         
          int
         
          .Parse(txtHoughCirclesMinRadius.Text);
                
         
          int
         
           maxRadius 
         
          =
         
           
         
          int
         
          .Parse(txtHoughCirclesMaxRadius.Text);
                StringBuilder sbResult 
         
          =
         
           
         
          new
         
           StringBuilder();
                
         
          //
         
          计算
         
          
    
         
                      Stopwatch sw 
         
          =
         
           
         
          new
         
           Stopwatch();
                sw.Start();
                CircleF[][] circles 
         
          =
         
           imageSourceGrayscale.HoughCircles(
         
          new
         
           Gray(param1), 
         
          new
         
           Gray(param2), dp, minDist, minRadius, maxRadius);
                sw.Stop();
                
         
          //
         
          显示
         
          
    
         
                      Image
         
          <
         
          Bgr, Byte
         
          >
         
           imageResult 
         
          =
         
           imageSourceGrayscale.Convert
         
          <
         
          Bgr, Byte
         
          >
         
          ();
                
         
          int
         
           circlesCount 
         
          =
         
           
         
          0
         
          ;
                
         
          foreach
         
           (CircleF[] cs 
         
          in
         
           circles)
                {
                    
         
          foreach
         
           (CircleF circle 
         
          in
         
           cs)
                    {
                        imageResult.Draw(circle, 
         
          new
         
           Bgr(255d, 0d, 0d), 
         
          4
         
          );
                        sbResult.AppendFormat(
         
          "
         
          圆心{0}半径{1}，
         
          "
         
          , circle.Center, circle.Radius);
                        circlesCount
         
          ++
         
          ;
                    }
                }
                pbResult.Image 
         
          =
         
           imageResult.Bitmap;
                
         
          //
         
          释放资源
         
          
    
         
                      imageResult.Dispose();
                
         
          //
         
          返回
         
          
    
         
                      
         
          return
         
           
         
          string
         
          .Format(
         
          "
         
          ·霍夫圆变换，用时{0:F05}毫秒，参数（累加器图像的最小分辨率：{1}，不同圆之间的最小距离：{2}，边缘阀值：{3}，累加器阀值：{4}，最小圆半径：{5}，最大圆半径：{6}），找到{7}个圆\r\n{8}
         
          "
         
          ,
                    sw.Elapsed.TotalMilliseconds, dp, minDist, param1, param2, minRadius, maxRadius, circlesCount, sbResult.Length 
         
          >
         
           
         
          0
         
           
         
          ?
         
           (sbResult.ToString() 
         
          +
         
           
         
          "
         
          \r\n
         
          "
         
          ) : 
         
          ""
         
          );
            }

![复制代码][copycode.gif]

**Harris角点**  
 cvCornerHarris函数检测的结果实际上是一幅包含Harris角点的浮点型单通道图像，可以使用类似下面的代码来计算包含Harris角点的图像：

![ExpandedBlockStart.gif][] Harris角点

![复制代码][copycode.gif]

//
         
          Harris角点
         
          
    
         
                  
         
          private
         
           
         
          string
         
           CornerHarrisFeatureDetect()
            {
                
         
          //
         
          获取参数
         
          
    
         
                      
         
          int
         
           blockSize 
         
          =
         
           
         
          int
         
          .Parse(txtCornerHarrisBlockSize.Text);
                
         
          int
         
           apertureSize 
         
          =
         
           
         
          int
         
          .Parse(txtCornerHarrisApertureSize.Text);
                
         
          double
         
           k 
         
          =
         
           
         
          double
         
          .Parse(txtCornerHarrisK.Text);
                
         
          //
         
          计算
         
          
    
         
                      Image
         
          <
         
          Gray, Single
         
          >
         
           imageDest 
         
          =
         
           
         
          new
         
           Image
         
          <
         
          Gray, 
         
          float
         
          >
         
          (imageSourceGrayscale.Size);
                Stopwatch sw 
         
          =
         
           
         
          new
         
           Stopwatch();
                sw.Start();
                CvInvoke.cvCornerHarris(imageSourceGrayscale.Ptr, imageDest.Ptr, blockSize, apertureSize, k);
                sw.Stop();
                
         
          //
         
          显示
         
          
    
         
                      pbResult.Image 
         
          =
         
           imageDest.Bitmap;
                
         
          //
         
          释放资源
         
          
    
         
                      imageDest.Dispose();
                
         
          //
         
          返回
         
          
    
         
                      
         
          return
         
           
         
          string
         
          .Format(
         
          "
         
          ·Harris角点，用时{0:F05}毫秒，参数（邻域大小：{1}，方形滤波器宽度：{2}，权重系数：{3}）\r\n
         
          "
         
          , sw.Elapsed.TotalMilliseconds, blockSize, apertureSize, k);
            }

![复制代码][copycode.gif]

如果要计算Harris角点列表，需要使用cvGoodFeatureToTrack函数，并传递适当的参数。

**ShiTomasi角点**  
 在默认情况下，cvGoodFeatureToTrack函数计算ShiTomasi角点；不过如果将参数use\_harris设置为非0值，那么它会计算harris角点。  
使用cvGoodFeatureToTrack函数的示例代码如下：

![ExpandedBlockStart.gif][] ShiTomasi角点

![复制代码][copycode.gif]

//
         
          ShiTomasi角点
         
          
    
         
                  
         
          private
         
           
         
          string
         
           CornerShiTomasiFeatureDetect()
            {
                
         
          //
         
          获取参数
         
          
    
         
                      
         
          int
         
           cornerCount 
         
          =
         
           
         
          int
         
          .Parse(txtGoodFeaturesCornerCount.Text);
                
         
          double
         
           qualityLevel 
         
          =
         
           
         
          double
         
          .Parse(txtGoodFeaturesQualityLevel.Text);
                
         
          double
         
           minDistance 
         
          =
         
           
         
          double
         
          .Parse(txtGoodFeaturesMinDistance.Text);
                
         
          int
         
           blockSize 
         
          =
         
           
         
          int
         
          .Parse(txtGoodFeaturesBlockSize.Text);
                
         
          bool
         
           useHarris 
         
          =
         
           cbGoodFeaturesUseHarris.Checked;
                
         
          double
         
           k 
         
          =
         
           
         
          double
         
          .Parse(txtGoodFeaturesK.Text);
                
         
          //
         
          计算
         
          
    
         
                      Stopwatch sw 
         
          =
         
           
         
          new
         
           Stopwatch();
                sw.Start();
                PointF[][] corners 
         
          =
         
           imageSourceGrayscale.GoodFeaturesToTrack(cornerCount, qualityLevel, minDistance, blockSize, useHarris, k);
                sw.Stop();
                
         
          //
         
          显示
         
          
    
         
                      Image
         
          <
         
          Bgr, Byte
         
          >
         
           imageResult 
         
          =
         
           imageSourceGrayscale.Convert
         
          <
         
          Bgr, Byte
         
          >
         
          ();
                
         
          int
         
           cornerCount2 
         
          =
         
           
         
          0
         
          ;
                StringBuilder sbResult 
         
          =
         
           
         
          new
         
           StringBuilder();
                
         
          int
         
           radius 
         
          =
         
           (
         
          int
         
          )(minDistance 
         
          /
         
           
         
          2
         
          ) 
         
          +
         
           
         
          1
         
          ;
                
         
          int
         
           thickness 
         
          =
         
           (
         
          int
         
          )(minDistance 
         
          /
         
           
         
          4
         
          ) 
         
          +
         
           
         
          1
         
          ;
                
         
          foreach
         
           (PointF[] cs 
         
          in
         
           corners)
                {
                    
         
          foreach
         
           (PointF p 
         
          in
         
           cs)
                    {
                        imageResult.Draw(
         
          new
         
           CircleF(p, radius), 
         
          new
         
           Bgr(255d, 0d, 0d), thickness);
                        cornerCount2
         
          ++
         
          ;
                        sbResult.AppendFormat(
         
          "
         
          {0}，
         
          "
         
          , p);
                    }
                }
                pbResult.Image 
         
          =
         
           imageResult.Bitmap;
                
         
          //
         
          释放资源
         
          
    
         
                      imageResult.Dispose();
                
         
          //
         
          返回
         
          
    
         
                      
         
          return
         
           
         
          string
         
          .Format(
         
          "
         
          ·ShiTomasi角点，用时{0:F05}毫秒，参数（最大角点数目：{1}，最小特征值：{2}，角点间的最小距离：{3}，邻域大小：{4}，角点类型：{5}，权重系数：{6}），检测到{7}个角点\r\n{8}
         
          "
         
          ,
                    sw.Elapsed.TotalMilliseconds, cornerCount, qualityLevel, minDistance, blockSize, useHarris 
         
          ?
         
           
         
          "
         
          Harris
         
          "
         
           : 
         
          "
         
          ShiTomasi
         
          "
         
          , k, cornerCount2, cornerCount2 
         
          >
         
           
         
          0
         
           
         
          ?
         
           (sbResult.ToString() 
         
          +
         
           
         
          "
         
          \r\n
         
          "
         
          ) : 
         
          ""
         
          );
            }

![复制代码][copycode.gif]

**亚像素级角点**  
 在检测亚像素级角点前，需要提供角点的初始为止，这些初始位置可以用本文给出的其他的角点检测方式来获取，不过使用GoodFeaturesToTrack得到的结果最方便直接使用。  
亚像素级角点检测的示例代码如下：

![ExpandedBlockStart.gif][] 亚像素级角点

![复制代码][copycode.gif]

//
         
          亚像素级角点
         
          
    
         
                  
         
          private
         
           
         
          string
         
           CornerSubPixFeatureDetect()
            {
                
         
          //
         
          获取参数
         
          
    
         
                      
         
          int
         
           winWidth 
         
          =
         
           
         
          int
         
          .Parse(txtCornerSubPixWinWidth.Text);
                
         
          int
         
           winHeight 
         
          =
         
           
         
          int
         
          .Parse(txtCornerSubPixWinHeight.Text);
                Size win 
         
          =
         
           
         
          new
         
           Size(winWidth, winHeight);
                
         
          int
         
           zeroZoneWidth 
         
          =
         
           
         
          int
         
          .Parse(txtCornerSubPixZeroZoneWidth.Text);
                
         
          int
         
           zeroZoneHeight 
         
          =
         
           
         
          int
         
          .Parse(txtCornerSubPixZeroZoneHeight.Text);
                Size zeroZone 
         
          =
         
           
         
          new
         
           Size(zeroZoneWidth, zeroZoneHeight);
                
         
          int
         
           maxIter
         
          =
         
          int
         
          .Parse(txtCornerSubPixMaxIter.Text);
                
         
          double
         
           epsilon
         
          =
         
          double
         
          .Parse(txtCornerSubPixEpsilon.Text);
                MCvTermCriteria criteria 
         
          =
         
           
         
          new
         
           MCvTermCriteria(maxIter, epsilon);
                
         
          //
         
          先计算得到易于跟踪的点（ShiTomasi角点）
         
          
    
         
                      
         
          int
         
           cornerCount 
         
          =
         
           
         
          int
         
          .Parse(txtGoodFeaturesCornerCount.Text);
                
         
          double
         
           qualityLevel 
         
          =
         
           
         
          double
         
          .Parse(txtGoodFeaturesQualityLevel.Text);
                
         
          double
         
           minDistance 
         
          =
         
           
         
          double
         
          .Parse(txtGoodFeaturesMinDistance.Text);
                
         
          int
         
           blockSize 
         
          =
         
           
         
          int
         
          .Parse(txtGoodFeaturesBlockSize.Text);
                
         
          bool
         
           useHarris 
         
          =
         
           cbGoodFeaturesUseHarris.Checked;
                
         
          double
         
           k 
         
          =
         
           
         
          double
         
          .Parse(txtGoodFeaturesK.Text);
                PointF[][] corners 
         
          =
         
           imageSourceGrayscale.GoodFeaturesToTrack(cornerCount, qualityLevel, minDistance, blockSize, useHarris, k);
                
         
          //
         
          计算
         
          
    
         
                      Stopwatch sw 
         
          =
         
           
         
          new
         
           Stopwatch();
                sw.Start();
                imageSourceGrayscale.FindCornerSubPix(corners, win, zeroZone, criteria);
                sw.Stop();
                
         
          //
         
          显示
         
          
    
         
                      Image
         
          <
         
          Bgr, Byte
         
          >
         
           imageResult 
         
          =
         
           imageSourceGrayscale.Convert
         
          <
         
          Bgr, Byte
         
          >
         
          ();
                
         
          int
         
           cornerCount2 
         
          =
         
           
         
          0
         
          ;
                StringBuilder sbResult 
         
          =
         
           
         
          new
         
           StringBuilder();
                
         
          int
         
           radius 
         
          =
         
           (
         
          int
         
          )(minDistance 
         
          /
         
           
         
          2
         
          ) 
         
          +
         
           
         
          1
         
          ;
                
         
          int
         
           thickness 
         
          =
         
           (
         
          int
         
          )(minDistance 
         
          /
         
           
         
          4
         
          ) 
         
          +
         
           
         
          1
         
          ;
                
         
          foreach
         
           (PointF[] cs 
         
          in
         
           corners)
                {
                    
         
          foreach
         
           (PointF p 
         
          in
         
           cs)
                    {
                        imageResult.Draw(
         
          new
         
           CircleF(p, radius), 
         
          new
         
           Bgr(255d, 0d, 0d), thickness);
                        cornerCount2
         
          ++
         
          ;
                        sbResult.AppendFormat(
         
          "
         
          {0}，
         
          "
         
          , p);
                    }
                }
                pbResult.Image 
         
          =
         
           imageResult.Bitmap;
                
         
          //
         
          释放资源
         
          
    
         
                      imageResult.Dispose();
                
         
          //
         
          返回
         
          
    
         
                      
         
          return
         
           
         
          string
         
          .Format(
         
          "
         
          ·亚像素级角点，用时{0:F05}毫秒，参数（搜索窗口：{1}，死区：{2}，最大迭代次数：{3}，亚像素值的精度：{4}），检测到{5}个角点\r\n{6}
         
          "
         
          ,
                    sw.Elapsed.TotalMilliseconds, win, zeroZone, maxIter, epsilon, cornerCount2, cornerCount2 
         
          >
         
           
         
          0
         
           
         
          ?
         
           (sbResult.ToString() 
         
          +
         
           
         
          "
         
          \r\n
         
          "
         
          ) : 
         
          ""
         
          );
            }

![复制代码][copycode.gif]

**SURF角点**  
OpenCv中的cvExtractSURF函数和EmguCv中的Image<TColor,TDepth>.ExtractSURF方法用于检测SURF角点。  
SURF角点检测的示例代码如下：

![ExpandedBlockStart.gif][] SURF角点

![复制代码][copycode.gif]

//
         
          SURF角点
         
          
    
         
                  
         
          private
         
           
         
          string
         
           SurfFeatureDetect()
            {
                
         
          //
         
          获取参数
         
          
    
         
                      
         
          bool
         
           getDescriptors 
         
          =
         
           cbSurfGetDescriptors.Checked;
                MCvSURFParams surfParam 
         
          =
         
           
         
          new
         
           MCvSURFParams();
                surfParam.extended
         
          =
         
          rbSurfBasicDescriptor.Checked 
         
          ?
         
           
         
          0
         
           : 
         
          1
         
          ;
                surfParam.hessianThreshold
         
          =
         
          double
         
          .Parse(txtSurfHessianThreshold.Text);
                surfParam.nOctaves
         
          =
         
          int
         
          .Parse(txtSurfNumberOfOctaves.Text);
                surfParam.nOctaveLayers
         
          =
         
          int
         
          .Parse(txtSurfNumberOfOctaveLayers.Text);
                
         
          //
         
          计算
         
          
    
         
                      SURFFeature[] features 
         
          =
         
           
         
          null
         
          ;
                MKeyPoint[] keyPoints 
         
          =
         
           
         
          null
         
          ;
                Stopwatch sw 
         
          =
         
           
         
          new
         
           Stopwatch();
                sw.Start();
                
         
          if
         
           (getDescriptors)
                    features 
         
          =
         
           imageSourceGrayscale.ExtractSURF(
         
          ref
         
           surfParam);
                
         
          else
         
          
                    keyPoints 
         
          =
         
           surfParam.DetectKeyPoints(imageSourceGrayscale, 
         
          null
         
          );
                sw.Stop();
                
         
          //
         
          显示
         
          
    
         
                      
         
          bool
         
           showDetail 
         
          =
         
           cbSurfShowDetail.Checked;
                Image
         
          <
         
          Bgr, Byte
         
          >
         
           imageResult 
         
          =
         
           imageSourceGrayscale.Convert
         
          <
         
          Bgr, Byte
         
          >
         
          ();
                StringBuilder sbResult 
         
          =
         
           
         
          new
         
           StringBuilder();
                
         
          int
         
           idx 
         
          =
         
           
         
          0
         
          ;
                
         
          if
         
           (getDescriptors)
                {
                    
         
          foreach
         
           (SURFFeature feature 
         
          in
         
           features)
                    {
                        imageResult.Draw(
         
          new
         
           CircleF(feature.Point.pt, 
         
          5
         
          ), 
         
          new
         
           Bgr(255d, 0d, 0d), 
         
          2
         
          );
                        
         
          if
         
           (showDetail)
                        {
                            sbResult.AppendFormat(
         
          "
         
          第{0}点（坐标：{1}，尺寸：{2}，方向：{3}°，hessian值：{4}，拉普拉斯标志：{5}，描述：[
         
          "
         
          ,
                                idx, feature.Point.pt, feature.Point.size, feature.Point.dir, feature.Point.hessian, feature.Point.laplacian);
                            
         
          foreach
         
           (
         
          float
         
           d 
         
          in
         
           feature.Descriptor)
                                sbResult.AppendFormat(
         
          "
         
          {0},
         
          "
         
          , d);
                            sbResult.Append(
         
          "
         
          ]），
         
          "
         
          );
                        }
                        idx
         
          ++
         
          ;
                    }
                }
                
         
          else
         
          
                {
                    
         
          foreach
         
           (MKeyPoint keypoint 
         
          in
         
           keyPoints)
                    {
                        imageResult.Draw(
         
          new
         
           CircleF(keypoint.Point, 
         
          5
         
          ), 
         
          new
         
           Bgr(255d, 0d, 0d), 
         
          2
         
          );
                        
         
          if
         
           (showDetail)
                            sbResult.AppendFormat(
         
          "
         
          第{0}点（坐标：{1}，尺寸：{2}，方向：{3}°，响应：{4}，octave：{5}），
         
          "
         
          ,
                                idx, keypoint.Point, keypoint.Size, keypoint.Angle, keypoint.Response, keypoint.Octave);
                        idx
         
          ++
         
          ;
                    }
                }
                pbResult.Image 
         
          =
         
           imageResult.Bitmap;
                
         
          //
         
          释放资源
         
          
    
         
                      imageResult.Dispose();
                
         
          //
         
          返回
         
          
    
         
                      
         
          return
         
           
         
          string
         
          .Format(
         
          "
         
          ·SURF角点，用时{0:F05}毫秒，参数（描述：{1}，hessian阀值：{2}，octave数目：{3}，每个octave的层数：{4}，检测到{5}个角点\r\n{6}
         
          "
         
          ,
                    sw.Elapsed.TotalMilliseconds, getDescriptors 
         
          ?
         
           (surfParam.extended 
         
          ==
         
           
         
          0
         
           
         
          ?
         
           
         
          "
         
          获取基本描述
         
          "
         
           : 
         
          "
         
          获取扩展描述
         
          "
         
          ) : 
         
          "
         
          不获取描述
         
          "
         
          , surfParam.hessianThreshold,
                    surfParam.nOctaves, surfParam.nOctaveLayers, getDescriptors 
         
          ?
         
           features.Length : keyPoints.Length, showDetail 
         
          ?
         
           sbResult.ToString() 
         
          +
         
           
         
          "
         
          \r\n
         
          "
         
           : 
         
          ""
         
          );
            }

![复制代码][copycode.gif]

**Star关键点**  
OpenCv中的cvGetStarKeypoints函数和EmguCv中的Image<TColor,TDepth>.GetStarKeypoints方法用于检测“星型”附近的点。  
Star关键点检测的示例代码如下：

![ExpandedBlockStart.gif][] Star关键点

![复制代码][copycode.gif]

//
         
          Star关键点
         
          
    
         
                  
         
          private
         
           
         
          string
         
           StarKeyPointFeatureDetect()
            {
                
         
          //
         
          获取参数
         
          
    
         
                      StarDetector starParam 
         
          =
         
           
         
          new
         
           StarDetector();
                starParam.MaxSize 
         
          =
         
           
         
          int
         
          .Parse((
         
          string
         
          )cmbStarMaxSize.SelectedItem);
                starParam.ResponseThreshold 
         
          =
         
           
         
          int
         
          .Parse(txtStarResponseThreshold.Text);
                starParam.LineThresholdProjected 
         
          =
         
           
         
          int
         
          .Parse(txtStarLineThresholdProjected.Text);
                starParam.LineThresholdBinarized 
         
          =
         
           
         
          int
         
          .Parse(txtStarLineThresholdBinarized.Text);
                starParam.SuppressNonmaxSize 
         
          =
         
           
         
          int
         
          .Parse(txtStarSuppressNonmaxSize.Text);
                
         
          //
         
          计算
         
          
    
         
                      Stopwatch sw 
         
          =
         
           
         
          new
         
           Stopwatch();
                sw.Start();
                MCvStarKeypoint[] keyPoints 
         
          =
         
           imageSourceGrayscale.GetStarKeypoints(
         
          ref
         
           starParam);
                sw.Stop();
                
         
          //
         
          显示
         
          
    
         
                      Image
         
          <
         
          Bgr, Byte
         
          >
         
           imageResult 
         
          =
         
           imageSourceGrayscale.Convert
         
          <
         
          Bgr, Byte
         
          >
         
          ();
                StringBuilder sbResult 
         
          =
         
           
         
          new
         
           StringBuilder();
                
         
          int
         
           idx 
         
          =
         
           
         
          0
         
          ;
                
         
          foreach
         
           (MCvStarKeypoint keypoint 
         
          in
         
           keyPoints)
                {
                    imageResult.Draw(
         
          new
         
           CircleF(
         
          new
         
           PointF(keypoint.pt.X, keypoint.pt.Y), keypoint.size 
         
          /
         
           
         
          2
         
          ), 
         
          new
         
           Bgr(255d, 0d, 0d), keypoint.size 
         
          /
         
           
         
          4
         
          );
                    sbResult.AppendFormat(
         
          "
         
          第{0}点（坐标：{1}，尺寸：{2}，强度：{3}），
         
          "
         
          , idx, keypoint.pt, keypoint.size, keypoint.response);
                    idx
         
          ++
         
          ;
                }
                pbResult.Image 
         
          =
         
           imageResult.Bitmap;
                
         
          //
         
          释放资源
         
          
    
         
                      imageResult.Dispose();
                
         
          //
         
          返回
         
          
    
         
                      
         
          return
         
           
         
          string
         
          .Format(
         
          "
         
          ·Star关键点，用时{0:F05}毫秒，参数（MaxSize：{1}，ResponseThreshold：{2}，LineThresholdProjected：{3}，LineThresholdBinarized：{4}，SuppressNonmaxSize：{5}），检测到{6}个关键点\r\n{7}
         
          "
         
          ,
                    sw.Elapsed.TotalMilliseconds, starParam.MaxSize, starParam.ResponseThreshold, starParam.LineThresholdProjected, starParam.LineThresholdBinarized, starParam.SuppressNonmaxSize, keyPoints.Length, keyPoints.Length 
         
          >
         
           
         
          0
         
           
         
          ?
         
           (sbResult.ToString() 
         
          +
         
           
         
          "
         
          \r\n
         
          "
         
          ) : 
         
          ""
         
          );
            }

![复制代码][copycode.gif]

**FAST角点检测**  
 FAST角点由E. Rosten教授提出，相比其他检测手段，这种方法的速度正如其名，相当的快。值得关注的是他所研究的理论都是属于实用类的，都很快。Rosten教授实现了FAST角点检测，并将其提供给了OpenCv，相当的有爱呀；不过OpenCv中的函数和Rosten教授的实现似乎有点点不太一样。遗憾的是，OpenCv中目前还没有FAST角点检测的文档。下面是我从Rosten的代码中找到的函数声明，可以看到粗略的参数说明。  
/\*  
The references are:

\* Machine learning for high-speed corner detection,  
  
E. Rosten and T. Drummond, ECCV 2006  
\* Faster and better: A machine learning approach to corner detection

E. Rosten, R. Porter and T. Drummond, PAMI, 2009

\*/  
void cvCornerFast( const CvArr\* image, int threshold, int N,

int nonmax\_suppression, int\* ret\_number\_of\_corners,  
CvPoint\*\* ret\_corners);

image: OpenCV image in which to detect corners. Must be 8 bit unsigned.

threshold: Threshold for detection (higher is fewer corners). 0--255

N: Arc length of detector, 9, 10, 11 or 12. 9 is usually best.

nonmax\_suppression: Whether to perform nonmaximal suppression.

ret\_number\_of\_corners: The number of detected corners is returned here.

ret\_corners: The corners are returned here.  
EmguCv中的 Image<TColor,TDepth>.GetFASTKeypoints方法也实现了FAST角点检测，不过参数少了一些，只有 threshold和nonmaxSupression，其中N我估计取的默认值9，但是返回的角点数目我不知道是怎么设置的。  
使用FAST角点检测的示例代码如下：

![ExpandedBlockStart.gif][] FAST关键点

![复制代码][copycode.gif]

//
         
          FAST关键点
         
          
    
         
                  
         
          private
         
           
         
          string
         
           FASTKeyPointFeatureDetect()
            {
                
         
          //
         
          获取参数
         
          
    
         
                      
         
          int
         
           threshold 
         
          =
         
           
         
          int
         
          .Parse(txtFASTThreshold.Text);
                
         
          bool
         
           nonmaxSuppression 
         
          =
         
           cbFASTNonmaxSuppression.Checked;
                
         
          bool
         
           showDetail 
         
          =
         
           cbFASTShowDetail.Checked;
                
         
          //
         
          计算
         
          
    
         
                      Stopwatch sw 
         
          =
         
           
         
          new
         
           Stopwatch();
                sw.Start();
                MKeyPoint[] keyPoints 
         
          =
         
           imageSourceGrayscale.GetFASTKeypoints(threshold, nonmaxSuppression);
                sw.Stop();
                
         
          //
         
          显示
         
          
    
         
                      Image
         
          <
         
          Bgr, Byte
         
          >
         
           imageResult 
         
          =
         
           imageSourceGrayscale.Convert
         
          <
         
          Bgr, Byte
         
          >
         
          ();
                StringBuilder sbResult 
         
          =
         
           
         
          new
         
           StringBuilder();
                
         
          int
         
           idx 
         
          =
         
           
         
          0
         
          ;
                
         
          foreach
         
           (MKeyPoint keypoint 
         
          in
         
           keyPoints)
                {
                    imageResult.Draw(
         
          new
         
           CircleF(keypoint.Point, (
         
          int
         
          )(keypoint.Size 
         
          /
         
           
         
          2
         
          )), 
         
          new
         
           Bgr(255d, 0d, 0d), (
         
          int
         
          )(keypoint.Size 
         
          /
         
           
         
          4
         
          ));
                    
         
          if
         
           (showDetail)
                        sbResult.AppendFormat(
         
          "
         
          第{0}点（坐标：{1}，尺寸：{2}，方向：{3}°，响应：{4}，octave：{5}），
         
          "
         
          ,
                                    idx, keypoint.Point, keypoint.Size, keypoint.Angle, keypoint.Response, keypoint.Octave);
                    idx
         
          ++
         
          ;
                }
                pbResult.Image 
         
          =
         
           imageResult.Bitmap;
                
         
          //
         
          释放资源
         
          
    
         
                      imageResult.Dispose();
                
         
          //
         
          返回
         
          
    
         
                      
         
          return
         
           
         
          string
         
          .Format(
         
          "
         
          ·FAST关键点，用时{0:F05}毫秒，参数（阀值：{1}，nonmaxSupression：{2}），检测到{3}个关键点\r\n{4}
         
          "
         
          ,
                    sw.Elapsed.TotalMilliseconds, threshold, nonmaxSuppression, keyPoints.Length, showDetail 
         
          ?
         
           (sbResult.ToString() 
         
          +
         
           
         
          "
         
          \r\n
         
          "
         
          ) : 
         
          ""
         
          );
            }

![复制代码][copycode.gif]

**Lepetit关键点**  
Lepetit关键点由Vincent Lepetit提出，可以在他的网站（[http://cvlab.epfl.ch/~vlepetit/][http_cvlab.epfl.ch_vlepetit]）上看到相关的论文等资料。EmguCv中的类LDetector实现了Lepetit关键点的检测。  
使用Lepetit关键点检测的示例代码如下：

![ExpandedBlockStart.gif][] Lepetit关键点

![复制代码][copycode.gif]

//
         
          Lepetit关键点
         
          
    
         
                  
         
          private
         
           
         
          string
         
           LepetitKeyPointFeatureDetect()
            {
                
         
          //
         
          获取参数
         
          
    
         
                      LDetector lepetitDetector 
         
          =
         
           
         
          new
         
           LDetector();
                lepetitDetector.BaseFeatureSize 
         
          =
         
           
         
          double
         
          .Parse(txtLepetitBaseFeatureSize.Text);
                lepetitDetector.ClusteringDistance 
         
          =
         
           
         
          double
         
          .Parse(txtLepetitClasteringDistance.Text);
                lepetitDetector.NOctaves 
         
          =
         
           
         
          int
         
          .Parse(txtLepetitNumberOfOctaves.Text);
                lepetitDetector.NViews 
         
          =
         
           
         
          int
         
          .Parse(txtLepetitNumberOfViews.Text);
                lepetitDetector.Radius 
         
          =
         
           
         
          int
         
          .Parse(txtLepetitRadius.Text);
                lepetitDetector.Threshold 
         
          =
         
           
         
          int
         
          .Parse(txtLepetitThreshold.Text);
                lepetitDetector.Verbose 
         
          =
         
           cbLepetitVerbose.Checked;
                
         
          int
         
           maxCount 
         
          =
         
           
         
          int
         
          .Parse(txtLepetitMaxCount.Text);
                
         
          bool
         
           scaleCoords 
         
          =
         
           cbLepetitScaleCoords.Checked;
                
         
          bool
         
           showDetail 
         
          =
         
           cbLepetitShowDetail.Checked;
                
         
          //
         
          计算
         
          
    
         
                      Stopwatch sw 
         
          =
         
           
         
          new
         
           Stopwatch();
                sw.Start();
                MKeyPoint[] keyPoints 
         
          =
         
           lepetitDetector.DetectKeyPoints(imageSourceGrayscale, maxCount, scaleCoords);
                sw.Stop();
                
         
          //
         
          显示
         
          
    
         
                      Image
         
          <
         
          Bgr, Byte
         
          >
         
           imageResult 
         
          =
         
           imageSourceGrayscale.Convert
         
          <
         
          Bgr, Byte
         
          >
         
          ();
                StringBuilder sbResult 
         
          =
         
           
         
          new
         
           StringBuilder();
                
         
          int
         
           idx 
         
          =
         
           
         
          0
         
          ;
                
         
          foreach
         
           (MKeyPoint keypoint 
         
          in
         
           keyPoints)
                {
                    
         
          //
         
          imageResult.Draw(new CircleF(keypoint.Point, (int)(keypoint.Size / 2)), new Bgr(255d, 0d, 0d), (int)(keypoint.Size / 4));
         
          
    
         
                          imageResult.Draw(
         
          new
         
           CircleF(keypoint.Point, 
         
          4
         
          ), 
         
          new
         
           Bgr(255d, 0d, 0d), 
         
          2
         
          );
                    
         
          if
         
           (showDetail)
                        sbResult.AppendFormat(
         
          "
         
          第{0}点（坐标：{1}，尺寸：{2}，方向：{3}°，响应：{4}，octave：{5}），
         
          "
         
          ,
                                    idx, keypoint.Point, keypoint.Size, keypoint.Angle, keypoint.Response, keypoint.Octave);
                    idx
         
          ++
         
          ;
                }
                pbResult.Image 
         
          =
         
           imageResult.Bitmap;
                
         
          //
         
          释放资源
         
          
    
         
                      imageResult.Dispose();
                
         
          //
         
          返回
         
          
    
         
                      
         
          return
         
           
         
          string
         
          .Format(
         
          "
         
          ·Lepetit关键点，用时{0:F05}毫秒，参数（基础特征尺寸：{1}，集群距离：{2}，阶数：{3}，视图数：{4}，半径：{5}，阀值：{6}，计算详细结果：{7}，最大关键点数目：{8}，缩放坐标：{9}），检测到{10}个关键点\r\n{11}
         
          "
         
          ,
                    sw.Elapsed.TotalMilliseconds, lepetitDetector.BaseFeatureSize, lepetitDetector.ClusteringDistance, lepetitDetector.NOctaves, lepetitDetector.NViews,
                    lepetitDetector.Radius, lepetitDetector.Threshold, lepetitDetector.Verbose, maxCount, scaleCoords, keyPoints.Length, showDetail 
         
          ?
         
           (sbResult.ToString() 
         
          +
         
           
         
          "
         
          \r\n
         
          "
         
          ) : 
         
          ""
         
          );
            }

![复制代码][copycode.gif]

**SIFT角点**  
SIFT角点是一种广泛使用的图像特征，可用于物体跟踪、图像匹配、图像拼接等领域，然而奇怪的是它并未被OpenCv实现。提出SIFT角点的 David Lowe教授已经用C和matlab实现了SIFT角点的检测，并开放了源代码，不过他的实现不方便直接使用。您可以在[http://www.cs.ubc.ca/~lowe/keypoints/][http_www.cs.ubc.ca_lowe_keypoints]看到SIFT的介绍、相关论文及David Lowe教授的实现代码。下面我要介绍由Andrea Vedaldi和Brian Fulkerson先生创建的vlfeat开源图像处理库，vlfeat库有C和matlab两种实现，其中包含了SIFT检测。您可以在[http://www.vlfeat.org/][http_www.vlfeat.org]下载到vlfeat库的代码、文档及可执行文件。  
使用vlfeat检测SIFT角点需要以下步骤：  
（1）用函数vl\_sift\_new()初始化SIFT过滤器对象，该过滤器对象可以反复用于多幅尺寸相同的图像；  
（2）用函数vl\_sift\_first\_octave()及vl\_sift\_process\_next()遍历缩放空间的每一阶，直到返回VL\_ERR\_EOF为止；  
（3）对于缩放空间的每一阶，用函数vl\_sift\_detect()来获取关键点；  
（4）对每个关键点，用函数vl\_sift\_calc\_keypoint\_orientations()来获取该点的方向；  
（5）对关键点的每个方向，用函数vl\_sift\_calc\_keypoint\_descriptor()来获取该方向的描述；  
（6）使用完之后，用函数vl\_sift\_delete()来释放资源；  
（7）如果要计算某个自定义关键点的描述，可以使用函数vl\_sift\_calc\_raw\_descriptor()。  
直接使用vlfeat中的SIFT角点检测示例代码如下：

![ExpandedBlockStart.gif][] 通过P/Invoke调用vlfeat函数来进行SIFT检测

![复制代码][copycode.gif]

//
         
          通过P/Invoke调用vlfeat函数来进行SIFT检测
         
          
    
         
                  
         
          unsafe
         
           
         
          private
         
           
         
          string
         
           SiftFeatureDetectByPinvoke(
         
          int
         
           noctaves, 
         
          int
         
           nlevels, 
         
          int
         
           o_min, 
         
          bool
         
           showDetail)
            {
                StringBuilder sbResult 
         
          =
         
           
         
          new
         
           StringBuilder();
                
         
          //
         
          初始化
         
          
    
         
                      IntPtr ptrSiftFilt 
         
          =
         
           VlFeatInvoke.vl_sift_new(imageSource.Width, imageSource.Height, noctaves, nlevels, o_min);
                
         
          if
         
           (ptrSiftFilt 
         
          ==
         
           IntPtr.Zero)
                    
         
          return
         
           
         
          "
         
          Sift特征检测：初始化失败。
         
          "
         
          ;
                
         
          //
         
          处理
         
          
    
         
                      Image
         
          <
         
          Gray, Single
         
          >
         
           imageSourceSingle 
         
          =
         
           imageSourceGrayscale.ConvertScale
         
          <
         
          Single
         
          >
         
          (1d, 0d);
                Image
         
          <
         
          Bgr, Byte
         
          >
         
           imageResult 
         
          =
         
           imageSourceGrayscale.Convert
         
          <
         
          Bgr, Byte
         
          >
         
          ();
                
         
          int
         
           pointCount 
         
          =
         
           
         
          0
         
          ;
                
         
          int
         
           idx 
         
          =
         
           
         
          0
         
          ;
                
         
          //
         
          依次遍历每一组
         
          
    
         
                      
         
          if
         
           (VlFeatInvoke.vl_sift_process_first_octave(ptrSiftFilt, imageSourceSingle.MIplImage.imageData) 
         
          !=
         
           VlFeatInvoke.VL_ERR_EOF)
                {
                    
         
          while
         
           (
         
          true
         
          )
                    {
                        
         
          //
         
          计算每组中的关键点
         
          
    
         
                              VlFeatInvoke.vl_sift_detect(ptrSiftFilt);
                        
         
          //
         
          遍历并绘制每个点
         
          
    
         
                              VlSiftFilt siftFilt 
         
          =
         
           (VlSiftFilt)Marshal.PtrToStructure(ptrSiftFilt, 
         
          typeof
         
          (VlSiftFilt));
                        pointCount 
         
          +=
         
           siftFilt.nkeys;
                        VlSiftKeypoint
         
          *
         
           pKeyPoints 
         
          =
         
           (VlSiftKeypoint
         
          *
         
          )siftFilt.keys.ToPointer();
                        
         
          for
         
           (
         
          int
         
           i 
         
          =
         
           
         
          0
         
          ; i 
         
          <
         
           siftFilt.nkeys; i
         
          ++
         
          )
                        {
                            VlSiftKeypoint keyPoint 
         
          =
         
           
         
          *
         
          pKeyPoints;
                            pKeyPoints
         
          ++
         
          ;
                            imageResult.Draw(
         
          new
         
           CircleF(
         
          new
         
           PointF(keyPoint.x, keyPoint.y), keyPoint.sigma 
         
          /
         
           
         
          2
         
          ), 
         
          new
         
           Bgr(255d, 0d, 0d), 
         
          2
         
          );
                            
         
          if
         
           (showDetail)
                                sbResult.AppendFormat(
         
          "
         
          第{0}点，坐标：({1},{2})，阶：{3}，缩放：{4}，s：{5}，
         
          "
         
          , idx, keyPoint.x, keyPoint.y, keyPoint.o, keyPoint.sigma, keyPoint.s);
                            idx
         
          ++
         
          ;
                            
         
          //
         
          计算并遍历每个点的方向
         
          
    
         
                                  
         
          double
         
          [] angles 
         
          =
         
           
         
          new
         
           
         
          double
         
          [
         
          4
         
          ];
                            
         
          int
         
           angleCount 
         
          =
         
           VlFeatInvoke.vl_sift_calc_keypoint_orientations(ptrSiftFilt, angles, 
         
          ref
         
           keyPoint);
                            
         
          if
         
           (showDetail)
                                sbResult.AppendFormat(
         
          "
         
          共{0}个方向，
         
          "
         
          , angleCount);
                            
         
          for
         
           (
         
          int
         
           j 
         
          =
         
           
         
          0
         
          ; j 
         
          <
         
           angleCount; j
         
          ++
         
          )
                            {
                                
         
          double
         
           angle 
         
          =
         
           angles[j];
                                
         
          if
         
           (showDetail)
                                    sbResult.AppendFormat(
         
          "
         
          【方向：{0}，描述：
         
          "
         
          , angle);
                                
         
          //
         
          计算每个方向的描述
         
          
    
         
                                      IntPtr ptrDescriptors 
         
          =
         
           Marshal.AllocHGlobal(
         
          128
         
           
         
          *
         
           
         
          sizeof
         
          (
         
          float
         
          ));
                                VlFeatInvoke.vl_sift_calc_keypoint_descriptor(ptrSiftFilt, ptrDescriptors, 
         
          ref
         
           keyPoint, angle);
                                
         
          float
         
          *
         
           pDescriptors 
         
          =
         
           (
         
          float
         
          *
         
          )ptrDescriptors.ToPointer();
                                
         
          for
         
           (
         
          int
         
           k 
         
          =
         
           
         
          0
         
          ; k 
         
          <
         
           
         
          128
         
          ; k
         
          ++
         
          )
                                {
                                    
         
          float
         
           descriptor 
         
          =
         
           
         
          *
         
          pDescriptors;
                                    pDescriptors
         
          ++
         
          ;
                                    
         
          if
         
           (showDetail)
                                        sbResult.AppendFormat(
         
          "
         
          {0},
         
          "
         
          , descriptor);
                                }
                                sbResult.Append(
         
          "
         
          】，
         
          "
         
          );
                                Marshal.FreeHGlobal(ptrDescriptors);
                            }
                        }
                        
         
          //
         
          下一阶
         
          
    
         
                              
         
          if
         
           (VlFeatInvoke.vl_sift_process_next_octave(ptrSiftFilt) 
         
          ==
         
           VlFeatInvoke.VL_ERR_EOF)
                            
         
          break
         
          ;
                    }
                }
                
         
          //
         
          显示
         
          
    
         
                      pbResult.Image 
         
          =
         
           imageResult.Bitmap;
                
         
          //
         
          释放资源
         
          
    
         
                      VlFeatInvoke.vl_sift_delete(ptrSiftFilt);
                imageSourceSingle.Dispose();
                imageResult.Dispose();
                
         
          //
         
          返回
         
          
    
         
                      
         
          return
         
           
         
          string
         
          .Format(
         
          "
         
          ·SIFT特征检测(P/Invoke)，用时：未统计，参数（阶数：{0}，每阶层数：{1}，最小阶索引：{2}），{3}个关键点\r\n{4}
         
          "
         
          ,
                    noctaves, nlevels, o_min, pointCount, showDetail 
         
          ?
         
           (sbResult.ToString() 
         
          +
         
           
         
          "
         
          \r\n
         
          "
         
          ) : 
         
          ""
         
          );
            }

![复制代码][copycode.gif]

要在.net中使用vlfeat还是不够方便，为此我对vlfeat中的SIFT角点检测部分进行了封装，将相关操作放到了类SiftDetector中。  
使用SiftDetector需要两至三步：  
（1）用构造函数初始化SiftDetector对象；  
（2）用Process方法计算特征；  
（3）视需要调用Dispose方法释放资源，或者等待垃圾回收器来自动释放资源。  
使用SiftDetector的示例代码如下：

![ExpandedBlockStart.gif][] 通过dotnet封装的SiftDetector类来进行SIFT检测

![复制代码][copycode.gif]

//
         
          通过dotnet封装的SiftDetector类来进行SIFT检测
         
          
    
         
                  
         
          private
         
           
         
          string
         
           SiftFeatureDetectByDotNet(
         
          int
         
           noctaves, 
         
          int
         
           nlevels, 
         
          int
         
           o_min, 
         
          bool
         
           showDetail)
            {
                
         
          //
         
          初始化对象
         
          
    
         
                      SiftDetector siftDetector 
         
          =
         
           
         
          new
         
           SiftDetector(imageSource.Size, noctaves, nlevels, o_min);
                
         
          //
         
          计算
         
          
    
         
                      Image
         
          <
         
          Gray, Single
         
          >
         
           imageSourceSingle 
         
          =
         
           imageSourceGrayscale.Convert
         
          <
         
          Gray, Single
         
          >
         
          ();
                Stopwatch sw 
         
          =
         
           
         
          new
         
           Stopwatch();
                sw.Start();
                List
         
          <
         
          SiftFeature
         
          >
         
           features 
         
          =
         
           siftDetector.Process(imageSourceSingle, showDetail 
         
          ?
         
           SiftDetectorResultType.Extended : SiftDetectorResultType.Basic);
                sw.Stop();
                
         
          //
         
          显示结果
         
          
    
         
                      Image
         
          <
         
          Bgr, Byte
         
          >
         
           imageResult 
         
          =
         
           imageSourceGrayscale.Convert
         
          <
         
          Bgr, Byte
         
          >
         
          ();
                StringBuilder sbResult 
         
          =
         
           
         
          new
         
           StringBuilder();
                
         
          int
         
           idx
         
          =
         
          0
         
          ;
                
         
          foreach
         
           (SiftFeature feature 
         
          in
         
           features)
                {
                    imageResult.Draw(
         
          new
         
           CircleF(
         
          new
         
           PointF(feature.keypoint.x, feature.keypoint.y), feature.keypoint.sigma 
         
          /
         
           
         
          2
         
          ), 
         
          new
         
           Bgr(255d, 0d, 0d), 
         
          2
         
          );
                    
         
          if
         
           (showDetail)
                    {
                        sbResult.AppendFormat(
         
          "
         
          第{0}点，坐标：({1},{2})，阶：{3}，缩放：{4}，s：{5}，
         
          "
         
          ,
                            idx, feature.keypoint.x, feature.keypoint.y, feature.keypoint.o, feature.keypoint.sigma, feature.keypoint.s);
                        sbResult.AppendFormat(
         
          "
         
          共{0}个方向，
         
          "
         
          , feature.keypointOrientations 
         
          !=
         
           
         
          null
         
           
         
          ?
         
           feature.keypointOrientations.Length : 
         
          0
         
          );
                        
         
          if
         
           (feature.keypointOrientations 
         
          !=
         
           
         
          null
         
          )
                        {
                            
         
          foreach
         
           (SiftKeyPointOrientation orientation 
         
          in
         
           feature.keypointOrientations)
                            {
                                
         
          if
         
           (orientation.descriptors 
         
          !=
         
           
         
          null
         
          )
                                {
                                    sbResult.AppendFormat(
         
          "
         
          【方向：{0}，描述：
         
          "
         
          , orientation.angle);
                                    
         
          foreach
         
           (
         
          float
         
           descriptor 
         
          in
         
           orientation.descriptors)
                                        sbResult.AppendFormat(
         
          "
         
          {0},
         
          "
         
          , descriptor);
                                }
                                
         
          else
         
          
                                    sbResult.AppendFormat(
         
          "
         
          【方向：{0}，
         
          "
         
          , orientation.angle);
                                sbResult.Append(
         
          "
         
          】，
         
          "
         
          );
                            }
                        }
                    }
                }
                pbResult.Image 
         
          =
         
           imageResult.Bitmap;
                
         
          //
         
          释放资源
         
          
    
         
                      siftDetector.Dispose();
                imageSourceSingle.Dispose();
                imageResult.Dispose();
                
         
          //
         
          返回
         
          
    
         
                      
         
          return
         
           
         
          string
         
          .Format(
         
          "
         
          ·SIFT特征检测(.net)，用时：{0:F05}毫秒，参数（阶数：{1}，每阶层数：{2}，最小阶索引：{3}），{4}个关键点\r\n{5}
         
          "
         
          ,
                    sw.Elapsed.TotalMilliseconds, noctaves, nlevels, o_min, features.Count, showDetail 
         
          ?
         
           (sbResult.ToString() 
         
          +
         
           
         
          "
         
          \r\n
         
          "
         
          ) : 
         
          ""
         
          );
            }

![复制代码][copycode.gif]

对vlfeat库中的SIFT部分封装代码如下所示：

![ExpandedBlockStart.gif][] 定义SiftDetector类

![复制代码][copycode.gif]

using
         
           System;
    
         
          using
         
           System.Collections.Generic;
    
         
          using
         
           System.Linq;
    
         
          using
         
           System.Text;
    
         
          using
         
           System.Runtime.InteropServices;
    
    
         
          namespace
         
           ImageProcessLearn
    {
        [StructLayoutAttribute(LayoutKind.Sequential)]
        
         
          public
         
           
         
          struct
         
           VlSiftKeypoint
        {
    
            
         
          ///
         
           int
         
          
    
         
                  
         
          public
         
           
         
          int
         
           o;
    
            
         
          ///
         
           int
         
          
    
         
                  
         
          public
         
           
         
          int
         
           ix;
    
            
         
          ///
         
           int
         
          
    
         
                  
         
          public
         
           
         
          int
         
           iy;
    
            
         
          ///
         
           int
         
          
    
         
                  
         
          public
         
           
         
          int
         
           @is;
    
            
         
          ///
         
           float
         
          
    
         
                  
         
          public
         
           
         
          float
         
           x;
    
            
         
          ///
         
           float
         
          
    
         
                  
         
          public
         
           
         
          float
         
           y;
    
            
         
          ///
         
           float
         
          
    
         
                  
         
          public
         
           
         
          float
         
           s;
    
            
         
          ///
         
           float
         
          
    
         
                  
         
          public
         
           
         
          float
         
           sigma;
        }
    
        [StructLayoutAttribute(LayoutKind.Sequential)]
        
         
          public
         
           
         
          struct
         
           VlSiftFilt
        {
    
            
         
          ///
         
           double
         
          
    
         
                  
         
          public
         
           
         
          double
         
           sigman;
    
            
         
          ///
         
           double
         
          
    
         
                  
         
          public
         
           
         
          double
         
           sigma0;
    
            
         
          ///
         
           double
         
          
    
         
                  
         
          public
         
           
         
          double
         
           sigmak;
    
            
         
          ///
         
           double
         
          
    
         
                  
         
          public
         
           
         
          double
         
           dsigma0;
    
            
         
          ///
         
           int
         
          
    
         
                  
         
          public
         
           
         
          int
         
           width;
    
            
         
          ///
         
           int
         
          
    
         
                  
         
          public
         
           
         
          int
         
           height;
    
            
         
          ///
         
           int
         
          
    
         
                  
         
          public
         
           
         
          int
         
           O;
    
            
         
          ///
         
           int
         
          
    
         
                  
         
          public
         
           
         
          int
         
           S;
    
            
         
          ///
         
           int
         
          
    
         
                  
         
          public
         
           
         
          int
         
           o_min;
    
            
         
          ///
         
           int
         
          
    
         
                  
         
          public
         
           
         
          int
         
           s_min;
    
            
         
          ///
         
           int
         
          
    
         
                  
         
          public
         
           
         
          int
         
           s_max;
    
            
         
          ///
         
           int
         
          
    
         
                  
         
          public
         
           
         
          int
         
           o_cur;
    
            
         
          ///
         
           vl_sift_pix*
         
          
    
         
                  
         
          public
         
           System.IntPtr temp;
    
            
         
          ///
         
           vl_sift_pix*
         
          
    
         
                  
         
          public
         
           System.IntPtr octave;
    
            
         
          ///
         
           vl_sift_pix*
         
          
    
         
                  
         
          public
         
           System.IntPtr dog;
    
            
         
          ///
         
           int
         
          
    
         
                  
         
          public
         
           
         
          int
         
           octave_width;
    
            
         
          ///
         
           int
         
          
    
         
                  
         
          public
         
           
         
          int
         
           octave_height;
    
            
         
          ///
         
           VlSiftKeypoint*
         
          
    
         
                  
         
          public
         
           System.IntPtr keys;
    
            
         
          ///
         
           int
         
          
    
         
                  
         
          public
         
           
         
          int
         
           nkeys;
    
            
         
          ///
         
           int
         
          
    
         
                  
         
          public
         
           
         
          int
         
           keys_res;
    
            
         
          ///
         
           double
         
          
    
         
                  
         
          public
         
           
         
          double
         
           peak_thresh;
    
            
         
          ///
         
           double
         
          
    
         
                  
         
          public
         
           
         
          double
         
           edge_thresh;
    
            
         
          ///
         
           double
         
          
    
         
                  
         
          public
         
           
         
          double
         
           norm_thresh;
    
            
         
          ///
         
           double
         
          
    
         
                  
         
          public
         
           
         
          double
         
           magnif;
    
            
         
          ///
         
           double
         
          
    
         
                  
         
          public
         
           
         
          double
         
           windowSize;
    
            
         
          ///
         
           vl_sift_pix*
         
          
    
         
                  
         
          public
         
           System.IntPtr grad;
    
            
         
          ///
         
           int
         
          
    
         
                  
         
          public
         
           
         
          int
         
           grad_o;
    
            
         
          ///
         
           
         
          <summary>
         
          
            
         
          ///
         
           获取SiftFilt指针；
            
         
          ///
         
           注意在使用完指针之后，需要用Marshal.FreeHGlobal释放内存。
            
         
          ///
         
           
         
          </summary>
         
          
            
         
          ///
         
           
         
          <returns></returns>
         
          
    
         
                  
         
          unsafe
         
           
         
          public
         
           IntPtr GetPtrOfVlSiftFilt()
            {
                IntPtr ptrSiftFilt 
         
          =
         
           Marshal.AllocHGlobal(
         
          sizeof
         
          (VlSiftFilt));
                Marshal.StructureToPtr(
         
          this
         
          , ptrSiftFilt, 
         
          true
         
          );
                
         
          return
         
           ptrSiftFilt;
            }
        }
    
        
         
          public
         
           
         
          class
         
           VlFeatInvoke
        {
            
         
          ///
         
           VL_ERR_MSG_LEN -> 1024
         
          
    
         
                  
         
          public
         
           
         
          const
         
           
         
          int
         
           VL_ERR_MSG_LEN 
         
          =
         
           
         
          1024
         
          ;
    
            
         
          ///
         
           VL_ERR_OK -> 0
         
          
    
         
                  
         
          public
         
           
         
          const
         
           
         
          int
         
           VL_ERR_OK 
         
          =
         
           
         
          0
         
          ;
    
            
         
          ///
         
           VL_ERR_OVERFLOW -> 1
         
          
    
         
                  
         
          public
         
           
         
          const
         
           
         
          int
         
           VL_ERR_OVERFLOW 
         
          =
         
           
         
          1
         
          ;
    
            
         
          ///
         
           VL_ERR_ALLOC -> 2
         
          
    
         
                  
         
          public
         
           
         
          const
         
           
         
          int
         
           VL_ERR_ALLOC 
         
          =
         
           
         
          2
         
          ;
    
            
         
          ///
         
           VL_ERR_BAD_ARG -> 3
         
          
    
         
                  
         
          public
         
           
         
          const
         
           
         
          int
         
           VL_ERR_BAD_ARG 
         
          =
         
           
         
          3
         
          ;
    
            
         
          ///
         
           VL_ERR_IO -> 4
         
          
    
         
                  
         
          public
         
           
         
          const
         
           
         
          int
         
           VL_ERR_IO 
         
          =
         
           
         
          4
         
          ;
    
            
         
          ///
         
           VL_ERR_EOF -> 5
         
          
    
         
                  
         
          public
         
           
         
          const
         
           
         
          int
         
           VL_ERR_EOF 
         
          =
         
           
         
          5
         
          ;
    
            
         
          ///
         
           VL_ERR_NO_MORE -> 5
         
          
    
         
                  
         
          public
         
           
         
          const
         
           
         
          int
         
           VL_ERR_NO_MORE 
         
          =
         
           
         
          5
         
          ;
    
            
         
          ///
         
           Return Type: VlSiftFilt*
            
         
          ///
         
          width: int
            
         
          ///
         
          height: int
            
         
          ///
         
          noctaves: int
            
         
          ///
         
          nlevels: int
            
         
          ///
         
          o_min: int
         
          
    
         
                  [DllImportAttribute(
         
          "
         
          vl.dll
         
          "
         
          , EntryPoint 
         
          =
         
           
         
          "
         
          vl_sift_new
         
          "
         
          )]
            
         
          public
         
           
         
          static
         
           
         
          extern
         
           System.IntPtr vl_sift_new(
         
          int
         
           width, 
         
          int
         
           height, 
         
          int
         
           noctaves, 
         
          int
         
           nlevels, 
         
          int
         
           o_min);
    
    
            
         
          ///
         
           Return Type: void
            
         
          ///
         
          f: VlSiftFilt*
         
          
    
         
                  [DllImportAttribute(
         
          "
         
          vl.dll
         
          "
         
          , EntryPoint 
         
          =
         
           
         
          "
         
          vl_sift_delete
         
          "
         
          )]
            
         
          public
         
           
         
          static
         
           
         
          extern
         
           
         
          void
         
           vl_sift_delete(IntPtr f);
    
    
            
         
          ///
         
           Return Type: int
            
         
          ///
         
          f: VlSiftFilt*
            
         
          ///
         
          im: vl_sift_pix*
         
          
    
         
                  [DllImportAttribute(
         
          "
         
          vl.dll
         
          "
         
          , EntryPoint 
         
          =
         
           
         
          "
         
          vl_sift_process_first_octave
         
          "
         
          )]
            
         
          public
         
           
         
          static
         
           
         
          extern
         
           
         
          int
         
           vl_sift_process_first_octave(IntPtr f, IntPtr im);
    
    
            
         
          ///
         
           Return Type: int
            
         
          ///
         
          f: VlSiftFilt*
         
          
    
         
                  [DllImportAttribute(
         
          "
         
          vl.dll
         
          "
         
          , EntryPoint 
         
          =
         
           
         
          "
         
          vl_sift_process_next_octave
         
          "
         
          )]
            
         
          public
         
           
         
          static
         
           
         
          extern
         
           
         
          int
         
           vl_sift_process_next_octave(IntPtr f);
    
    
            
         
          ///
         
           Return Type: void
            
         
          ///
         
          f: VlSiftFilt*
         
          
    
         
                  [DllImportAttribute(
         
          "
         
          vl.dll
         
          "
         
          , EntryPoint 
         
          =
         
           
         
          "
         
          vl_sift_detect
         
          "
         
          )]
            
         
          public
         
           
         
          static
         
           
         
          extern
         
           
         
          void
         
           vl_sift_detect(IntPtr f);
    
    
            
         
          ///
         
           Return Type: int
            
         
          ///
         
          f: VlSiftFilt*
            
         
          ///
         
          angles: double*
            
         
          ///
         
          k: VlSiftKeypoint*
         
          
    
         
                  [DllImportAttribute(
         
          "
         
          vl.dll
         
          "
         
          , EntryPoint 
         
          =
         
           
         
          "
         
          vl_sift_calc_keypoint_orientations
         
          "
         
          )]
            
         
          public
         
           
         
          static
         
           
         
          extern
         
           
         
          int
         
           vl_sift_calc_keypoint_orientations(IntPtr f, 
         
          double
         
          [] angles, 
         
          ref
         
           VlSiftKeypoint k);
    
    
            
         
          ///
         
           Return Type: void
            
         
          ///
         
          f: VlSiftFilt*
            
         
          ///
         
          descr: vl_sift_pix*
            
         
          ///
         
          k: VlSiftKeypoint*
            
         
          ///
         
          angle: double
         
          
    
         
                  [DllImportAttribute(
         
          "
         
          vl.dll
         
          "
         
          , EntryPoint 
         
          =
         
           
         
          "
         
          vl_sift_calc_keypoint_descriptor
         
          "
         
          )]
            
         
          public
         
           
         
          static
         
           
         
          extern
         
           
         
          void
         
           vl_sift_calc_keypoint_descriptor(IntPtr f, IntPtr descr, 
         
          ref
         
           VlSiftKeypoint k, 
         
          double
         
           angle);
    
    
            
         
          ///
         
           Return Type: void
            
         
          ///
         
          f: VlSiftFilt*
            
         
          ///
         
          image: vl_sift_pix*
            
         
          ///
         
          descr: vl_sift_pix*
            
         
          ///
         
          widht: int
            
         
          ///
         
          height: int
            
         
          ///
         
          x: double
            
         
          ///
         
          y: double
            
         
          ///
         
          s: double
            
         
          ///
         
          angle0: double
         
          
    
         
                  [DllImportAttribute(
         
          "
         
          vl.dll
         
          "
         
          , EntryPoint 
         
          =
         
           
         
          "
         
          vl_sift_calc_raw_descriptor
         
          "
         
          )]
            
         
          public
         
           
         
          static
         
           
         
          extern
         
           
         
          void
         
           vl_sift_calc_raw_descriptor(IntPtr f, IntPtr image, IntPtr descr, 
         
          int
         
           widht, 
         
          int
         
           height, 
         
          double
         
           x, 
         
          double
         
           y, 
         
          double
         
           s, 
         
          double
         
           angle0);
    
    
            
         
          ///
         
           Return Type: void
            
         
          ///
         
          f: VlSiftFilt*
            
         
          ///
         
          k: VlSiftKeypoint*
            
         
          ///
         
          x: double
            
         
          ///
         
          y: double
            
         
          ///
         
          sigma: double
         
          
    
         
                  [DllImportAttribute(
         
          "
         
          vl.dll
         
          "
         
          , EntryPoint 
         
          =
         
           
         
          "
         
          vl_sift_keypoint_init
         
          "
         
          )]
            
         
          public
         
           
         
          static
         
           
         
          extern
         
           
         
          void
         
           vl_sift_keypoint_init(IntPtr f, 
         
          ref
         
           VlSiftKeypoint k, 
         
          double
         
           x, 
         
          double
         
           y, 
         
          double
         
           sigma);
        }
    }
        SiftDetector类的实现代码如下所示：
    
         
          using
         
           System;
    
         
          using
         
           System.Collections.Generic;
    
         
          using
         
           System.Linq;
    
         
          using
         
           System.Text;
    
         
          using
         
           System.Drawing;
    
         
          using
         
           System.Runtime.InteropServices;
    
         
          using
         
           Emgu.CV;
    
         
          using
         
           Emgu.CV.Structure;
    
    
         
          namespace
         
           ImageProcessLearn
    {
        
         
          ///
         
           
         
          <summary>
         
          
        
         
          ///
         
           SIFT检测器
        
         
          ///
         
           
         
          </summary>
         
          
    
         
              
         
          public
         
           
         
          class
         
           SiftDetector : IDisposable
        {
            
         
          //
         
          成员变量
         
          
    
         
                  
         
          private
         
           IntPtr ptrSiftFilt;
    
            
         
          //
         
          属性
         
          
    
         
                  
         
          ///
         
           
         
          <summary>
         
          
            
         
          ///
         
           SiftFilt指针
            
         
          ///
         
           
         
          </summary>
         
          
    
         
                  
         
          public
         
           IntPtr PtrSiftFilt
            {
                
         
          get
         
          
                {
                    
         
          return
         
           ptrSiftFilt;
                }
            }
    
            
         
          ///
         
           
         
          <summary>
         
          
            
         
          ///
         
           获取SIFT检测器中的SiftFilt
            
         
          ///
         
           
         
          </summary>
         
          
    
         
                  
         
          public
         
           VlSiftFilt SiftFilt
            {
                
         
          get
         
          
                {
                    
         
          return
         
           (VlSiftFilt)Marshal.PtrToStructure(ptrSiftFilt, 
         
          typeof
         
          (VlSiftFilt));
                }
            }
    
            
         
          ///
         
           
         
          <summary>
         
          
            
         
          ///
         
           构造函数
            
         
          ///
         
           
         
          </summary>
         
          
            
         
          ///
         
           
         
          <param name="width">
         
          图像的宽度
         
          </param>
         
          
            
         
          ///
         
           
         
          <param name="height">
         
          图像的高度
         
          </param>
         
          
            
         
          ///
         
           
         
          <param name="noctaves">
         
          阶数
         
          </param>
         
          
            
         
          ///
         
           
         
          <param name="nlevels">
         
          每一阶的层数
         
          </param>
         
          
            
         
          ///
         
           
         
          <param name="o_min">
         
          最小阶的索引
         
          </param>
         
          
    
         
                  
         
          public
         
           SiftDetector(
         
          int
         
           width, 
         
          int
         
           height, 
         
          int
         
           noctaves, 
         
          int
         
           nlevels, 
         
          int
         
           o_min)
            {
                ptrSiftFilt 
         
          =
         
           VlFeatInvoke.vl_sift_new(width, height, noctaves, nlevels, o_min);
            }
            
         
          public
         
           SiftDetector(
         
          int
         
           width, 
         
          int
         
           height)
                : 
         
          this
         
          (width, height, 
         
          4
         
          , 
         
          2
         
          , 
         
          0
         
          )
            { }
            
         
          public
         
           SiftDetector(Size size, 
         
          int
         
           noctaves, 
         
          int
         
           nlevels, 
         
          int
         
           o_min)
                : 
         
          this
         
          (size.Width, size.Height, noctaves, nlevels, o_min)
            { }
            
         
          public
         
           SiftDetector(Size size)
                : 
         
          this
         
          (size.Width, size.Height, 
         
          4
         
          , 
         
          2
         
          , 
         
          0
         
          )
            { }
    
            
         
          ///
         
           
         
          <summary>
         
          
            
         
          ///
         
           进行SIFT检测，并返回检测的结果
            
         
          ///
         
           
         
          </summary>
         
          
            
         
          ///
         
           
         
          <param name="im">
         
          单通道浮点型图像数据，图像数据不必归一化到区间[0,1]
         
          </param>
         
          
            
         
          ///
         
           
         
          <param name="resultType">
         
          SIFT检测的结果类型
         
          </param>
         
          
            
         
          ///
         
           
         
          <returns>
         
          返回SIFT检测结果——SIFT特征列表；如果检测失败，返回null。
         
          </returns>
         
          
    
         
                  
         
          unsafe
         
           
         
          public
         
           List
         
          <
         
          SiftFeature
         
          >
         
           Process(IntPtr im, SiftDetectorResultType resultType)
            {
                
         
          //
         
          定义变量
         
          
    
         
                      List
         
          <
         
          SiftFeature
         
          >
         
           features 
         
          =
         
           
         
          null
         
          ;      
         
          //
         
          检测结果：SIFT特征列表
         
          
    
         
                      VlSiftFilt siftFilt;                    
         
          //
    
         
                      VlSiftKeypoint
         
          *
         
           pKeyPoints;             
         
          //
         
          指向关键点的指针
         
          
    
         
                      VlSiftKeypoint keyPoint;                
         
          //
         
          关键点
         
          
    
         
                      SiftKeyPointOrientation[] orientations; 
         
          //
         
          关键点对应的方向及描述
         
          
    
         
                      
         
          double
         
          [] angles 
         
          =
         
           
         
          new
         
           
         
          double
         
          [
         
          4
         
          ];        
         
          //
         
          关键点对应的方向（角度）
         
          
    
         
                      
         
          int
         
           angleCount;                         
         
          //
         
          某个关键点的方向数目
         
          
    
         
                      
         
          double
         
           angle;                           
         
          //
         
          方向
         
          
    
         
                      
         
          float
         
          [] descriptors;                    
         
          //
         
          关键点某个方向的描述
         
          
    
         
                      IntPtr ptrDescriptors 
         
          =
         
           Marshal.AllocHGlobal(
         
          128
         
           
         
          *
         
           
         
          sizeof
         
          (
         
          float
         
          ));  
         
          //
         
          指向描述的缓冲区指针
                
         
          //
         
          依次遍历每一阶
         
          
    
         
                      
         
          if
         
           (VlFeatInvoke.vl_sift_process_first_octave(ptrSiftFilt, im) 
         
          !=
         
           VlFeatInvoke.VL_ERR_EOF)
                {
                    features 
         
          =
         
           
         
          new
         
           List
         
          <
         
          SiftFeature
         
          >
         
          (
         
          100
         
          );
                    
         
          while
         
           (
         
          true
         
          )
                    {
                        
         
          //
         
          计算每组中的关键点
         
          
    
         
                              VlFeatInvoke.vl_sift_detect(ptrSiftFilt);
                        
         
          //
         
          遍历每个点
         
          
    
         
                              siftFilt 
         
          =
         
           (VlSiftFilt)Marshal.PtrToStructure(ptrSiftFilt, 
         
          typeof
         
          (VlSiftFilt));
                        pKeyPoints 
         
          =
         
           (VlSiftKeypoint
         
          *
         
          )siftFilt.keys.ToPointer();
                        
         
          for
         
           (
         
          int
         
           i 
         
          =
         
           
         
          0
         
          ; i 
         
          <
         
           siftFilt.nkeys; i
         
          ++
         
          )
                        {
                            keyPoint 
         
          =
         
           
         
          *
         
          pKeyPoints;
                            pKeyPoints
         
          ++
         
          ;
                            orientations 
         
          =
         
           
         
          null
         
          ;
                            
         
          if
         
           (resultType 
         
          ==
         
           SiftDetectorResultType.Normal 
         
          ||
         
           resultType 
         
          ==
         
           SiftDetectorResultType.Extended)
                            {
                                
         
          //
         
          计算并遍历每个点的方向
         
          
    
         
                                      angleCount 
         
          =
         
           VlFeatInvoke.vl_sift_calc_keypoint_orientations(ptrSiftFilt, angles, 
         
          ref
         
           keyPoint);
                                orientations 
         
          =
         
           
         
          new
         
           SiftKeyPointOrientation[angleCount];
                                
         
          for
         
           (
         
          int
         
           j 
         
          =
         
           
         
          0
         
          ; j 
         
          <
         
           angleCount; j
         
          ++
         
          )
                                {
                                    angle 
         
          =
         
           angles[j];
                                    descriptors 
         
          =
         
           
         
          null
         
          ;
                                    
         
          if
         
           (resultType 
         
          ==
         
           SiftDetectorResultType.Extended)
                                    {
                                        
         
          //
         
          计算每个方向的描述
         
          
    
         
                                              VlFeatInvoke.vl_sift_calc_keypoint_descriptor(ptrSiftFilt, ptrDescriptors, 
         
          ref
         
           keyPoint, angle);
                                        descriptors 
         
          =
         
           
         
          new
         
           
         
          float
         
          [
         
          128
         
          ];
                                        Marshal.Copy(ptrDescriptors, descriptors, 
         
          0
         
          , 
         
          128
         
          );
                                    }
                                    orientations[j] 
         
          =
         
           
         
          new
         
           SiftKeyPointOrientation(angle, descriptors);  
         
          //
         
          保存关键点方向和描述
         
          
    
         
                                      }
                            }
                            features.Add(
         
          new
         
           SiftFeature(keyPoint, orientations));  
         
          //
         
          将得到的特征添加到列表中
         
          
    
         
                              }
                        
         
          //
         
          下一阶
         
          
    
         
                              
         
          if
         
           (VlFeatInvoke.vl_sift_process_next_octave(ptrSiftFilt) 
         
          ==
         
           VlFeatInvoke.VL_ERR_EOF)
                            
         
          break
         
          ;
                    }
                }
                
         
          //
         
          释放资源
         
          
    
         
                      Marshal.FreeHGlobal(ptrDescriptors);
                
         
          //
         
          返回
         
          
    
         
                      
         
          return
         
           features;
            }
    
            
         
          ///
         
           
         
          <summary>
         
          
            
         
          ///
         
           进行基本的SIFT检测，并返回关键点列表
            
         
          ///
         
           
         
          </summary>
         
          
            
         
          ///
         
           
         
          <param name="im">
         
          单通道浮点型图像数据，图像数据不必归一化到区间[0,1]
         
          </param>
         
          
            
         
          ///
         
           
         
          <returns>
         
          返回关键点列表；如果获取失败，返回null。
         
          </returns>
         
          
    
         
                  
         
          public
         
           List
         
          <
         
          SiftFeature
         
          >
         
           Process(IntPtr im)
            {
                
         
          return
         
           Process(im, SiftDetectorResultType.Basic);
            }
    
            
         
          ///
         
           
         
          <summary>
         
          
            
         
          ///
         
           进行SIFT检测，并返回检测的结果
            
         
          ///
         
           
         
          </summary>
         
          
            
         
          ///
         
           
         
          <param name="image">
         
          图像
         
          </param>
         
          
            
         
          ///
         
           
         
          <param name="resultType">
         
          SIFT检测的结果类型
         
          </param>
         
          
            
         
          ///
         
           
         
          <returns>
         
          返回SIFT检测结果——SIFT特征列表；如果检测失败，返回null。
         
          </returns>
         
          
    
         
                  
         
          public
         
           List
         
          <
         
          SiftFeature
         
          >
         
           Process(Image
         
          <
         
          Gray, Single
         
          >
         
           image, SiftDetectorResultType resultType)
            {
                
         
          if
         
           (image.Width 
         
          !=
         
           SiftFilt.width 
         
          ||
         
           image.Height 
         
          !=
         
           SiftFilt.height)
                    
         
          throw
         
           
         
          new
         
           ArgumentException(
         
          "
         
          图像的尺寸和构造函数中指定的尺寸不一致。
         
          "
         
          , 
         
          "
         
          image
         
          "
         
          );
                
         
          return
         
           Process(image.MIplImage.imageData, resultType);
            }
    
            
         
          ///
         
           
         
          <summary>
         
          
            
         
          ///
         
           进行基本的SIFT检测，并返回检测的结果
            
         
          ///
         
           
         
          </summary>
         
          
            
         
          ///
         
           
         
          <param name="image">
         
          图像
         
          </param>
         
          
            
         
          ///
         
           
         
          <returns>
         
          返回SIFT检测结果——SIFT特征列表；如果检测失败，返回null。
         
          </returns>
         
          
    
         
                  
         
          public
         
           List
         
          <
         
          SiftFeature
         
          >
         
           Process(Image
         
          <
         
          Gray, Single
         
          >
         
           image)
            {
                
         
          return
         
           Process(image, SiftDetectorResultType.Basic);
            }
    
            
         
          ///
         
           
         
          <summary>
         
          
            
         
          ///
         
           释放资源
            
         
          ///
         
           
         
          </summary>
         
          
    
         
                  
         
          public
         
           
         
          void
         
           Dispose()
            {
                
         
          if
         
           (ptrSiftFilt 
         
          !=
         
           IntPtr.Zero)
                    VlFeatInvoke.vl_sift_delete(ptrSiftFilt);
            }
        }
    
        
         
          ///
         
           
         
          <summary>
         
          
        
         
          ///
         
           SIFT特征
        
         
          ///
         
           
         
          </summary>
         
          
    
         
              
         
          public
         
           
         
          struct
         
           SiftFeature
        {
            
         
          public
         
           VlSiftKeypoint keypoint;                            
         
          //
         
          关键点
         
          
    
         
                  
         
          public
         
           SiftKeyPointOrientation[] keypointOrientations;     
         
          //
         
          关键点的方向及方向对应的描述
         
          
    
         
          
            
         
          public
         
           SiftFeature(VlSiftKeypoint keypoint)
                : 
         
          this
         
          (keypoint, 
         
          null
         
          )
            {
            }
    
            
         
          public
         
           SiftFeature(VlSiftKeypoint keypoint, SiftKeyPointOrientation[] keypointOrientations)
            {
                
         
          this
         
          .keypoint 
         
          =
         
           keypoint;
                
         
          this
         
          .keypointOrientations 
         
          =
         
           keypointOrientations;
            }
        }
    
        
         
          ///
         
           
         
          <summary>
         
          
        
         
          ///
         
           Sift关键点的方向及描述
        
         
          ///
         
           
         
          </summary>
         
          
    
         
              
         
          public
         
           
         
          struct
         
           SiftKeyPointOrientation
        {
            
         
          public
         
           
         
          double
         
           angle;            
         
          //
         
          方向
         
          
    
         
                  
         
          public
         
           
         
          float
         
          [] descriptors;     
         
          //
         
          描述
         
          
    
         
          
            
         
          public
         
           SiftKeyPointOrientation(
         
          double
         
           angle)
                : 
         
          this
         
          (angle, 
         
          null
         
          )
            {
            }
    
            
         
          public
         
           SiftKeyPointOrientation(
         
          double
         
           angle, 
         
          float
         
          [] descriptors)
            {
                
         
          this
         
          .angle 
         
          =
         
           angle;
                
         
          this
         
          .descriptors 
         
          =
         
           descriptors;
            }
        }
    
        
         
          ///
         
           
         
          <summary>
         
          
        
         
          ///
         
           SIFT检测的结果
        
         
          ///
         
           
         
          </summary>
         
          
    
         
              
         
          public
         
           
         
          enum
         
           SiftDetectorResultType
        {
            Basic,      
         
          //
         
          基本：仅包含关键点
         
          
    
         
                  Normal,     
         
          //
         
          正常：包含关键点、方向
         
          
    
         
                  Extended    
         
          //
         
          扩展：包含关键点、方向以及描述
         
          
    
         
              }
    }

![复制代码][copycode.gif]

**MSER区域**  
 OpenCv中的函数cvExtractMSER以及EmguCv中的Image<TColor,TDepth>.ExtractMSER方法实现了MSER区域的检测。由于OpenCv的文档中目前还没有cvExtractMSER这一部分，大家如果要看文档的话，可以先去看EmguCv的文档。  
需要注意的是MSER区域的检测结果是区域中所有的点序列。例如检测到3个区域，其中一个区域是从(0,0)到(2,1)的矩形，那么结果点序列为：(0,0),(1,0),(2,0),(2,1),(1,1),(0,1)。  
MSER区域检测的示例代码如下：

![ExpandedBlockStart.gif][] MSER（区域）特征检测

![复制代码][copycode.gif]

//
         
          MSER（区域）特征检测
         
          
    
         
                  
         
          private
         
           
         
          string
         
           MserFeatureDetect()
            {
                
         
          //
         
          获取参数
         
          
    
         
                      MCvMSERParams mserParam 
         
          =
         
           
         
          new
         
           MCvMSERParams();
                mserParam.delta 
         
          =
         
           
         
          int
         
          .Parse(txtMserDelta.Text);
                mserParam.maxArea 
         
          =
         
           
         
          int
         
          .Parse(txtMserMaxArea.Text);
                mserParam.minArea 
         
          =
         
           
         
          int
         
          .Parse(txtMserMinArea.Text);
                mserParam.maxVariation 
         
          =
         
           
         
          float
         
          .Parse(txtMserMaxVariation.Text);
                mserParam.minDiversity 
         
          =
         
           
         
          float
         
          .Parse(txtMserMinDiversity.Text);
                mserParam.maxEvolution 
         
          =
         
           
         
          int
         
          .Parse(txtMserMaxEvolution.Text);
                mserParam.areaThreshold 
         
          =
         
           
         
          double
         
          .Parse(txtMserAreaThreshold.Text);
                mserParam.minMargin 
         
          =
         
           
         
          double
         
          .Parse(txtMserMinMargin.Text);
                mserParam.edgeBlurSize 
         
          =
         
           
         
          int
         
          .Parse(txtMserEdgeBlurSize.Text);
                
         
          bool
         
           showDetail 
         
          =
         
           cbMserShowDetail.Checked;
                
         
          //
         
          计算
         
          
    
         
                      Stopwatch sw 
         
          =
         
           
         
          new
         
           Stopwatch();
                sw.Start();
                MemStorage storage 
         
          =
         
           
         
          new
         
           MemStorage();
                Seq
         
          <
         
          Point
         
          >
         
          [] regions 
         
          =
         
           imageSource.ExtractMSER(
         
          null
         
          , 
         
          ref
         
           mserParam, storage);
                sw.Stop();
                
         
          //
         
          显示
         
          
    
         
                      Image
         
          <
         
          Bgr, Byte
         
          >
         
           imageResult 
         
          =
         
           imageSourceGrayscale.Convert
         
          <
         
          Bgr, Byte
         
          >
         
          ();
                StringBuilder sbResult 
         
          =
         
           
         
          new
         
           StringBuilder();
                
         
          int
         
           idx 
         
          =
         
           
         
          0
         
          ;
                
         
          foreach
         
           (Seq
         
          <
         
          Point
         
          >
         
           region 
         
          in
         
           regions)
                {
                    imageResult.DrawPolyline(region.ToArray(), 
         
          true
         
          , 
         
          new
         
           Bgr(255d, 0d, 0d), 
         
          2
         
          );
                    
         
          if
         
           (showDetail)
                    {
                        sbResult.AppendFormat(
         
          "
         
          第{0}区域，包含{1}个顶点（
         
          "
         
          , idx, region.Total);
                        
         
          foreach
         
           (Point pt 
         
          in
         
           region)
                            sbResult.AppendFormat(
         
          "
         
          {0},
         
          "
         
          , pt);
                        sbResult.Append(
         
          "
         
          ）\r\n
         
          "
         
          );
                    }
                    idx
         
          ++
         
          ;
                }
                pbResult.Image 
         
          =
         
           imageResult.Bitmap;
                
         
          //
         
          释放资源
         
          
    
         
                      imageResult.Dispose();
                storage.Dispose();
                
         
          //
         
          返回
         
          
    
         
                      
         
          return
         
           
         
          string
         
          .Format(
         
          "
         
          ·MSER区域，用时{0:F05}毫秒，参数（delta：{1}，maxArea：{2}，minArea：{3}，maxVariation：{4}，minDiversity：{5}，maxEvolution：{6}，areaThreshold：{7}，minMargin：{8}，edgeBlurSize：{9}），检测到{10}个区域\r\n{11}
         
          "
         
          ,
                    sw.Elapsed.TotalMilliseconds, mserParam.delta, mserParam.maxArea, mserParam.minArea, mserParam.maxVariation, mserParam.minDiversity,
                    mserParam.maxEvolution, mserParam.areaThreshold, mserParam.minMargin, mserParam.edgeBlurSize, regions.Length, showDetail 
         
          ?
         
           sbResult.ToString() : 
         
          ""
         
          );
            }

![复制代码][copycode.gif]

**各种特征检测方法性能对比**  
 上面介绍了这么多的特征检测方法，那么它们的性能到底如何呢？因为它们的参数设置对处理时间及结果的影响很大，我们在这里基本都使用默认参数处理同一幅图像。在我机器上的处理结果见下表：

<table> 
 <tbody> 
  <tr> 
   <td>特征</td> 
   <td>用时（毫秒）</td> 
   <td>特征数目</td> 
  </tr> 
  <tr> 
   <td>Sobel算子</td> 
   <td>5.99420</td> 
   <td>n/a</td> 
  </tr> 
  <tr> 
   <td>拉普拉斯算子</td> 
   <td>3.13440</td> 
   <td>n/a</td> 
  </tr> 
  <tr> 
   <td>Canny算子</td> 
   <td>3.41160</td> 
   <td>n/a</td> 
  </tr> 
  <tr> 
   <td>霍夫线变换</td> 
   <td>13.70790</td> 
   <td>10</td> 
  </tr> 
  <tr> 
   <td>霍夫圆变换</td> 
   <td>78.07720</td> 
   <td>0</td> 
  </tr> 
  <tr> 
   <td>Harris角点</td> 
   <td>9.41750</td> 
   <td>n/a</td> 
  </tr> 
  <tr> 
   <td>ShiTomasi角点</td> 
   <td>16.98390</td> 
   <td>18</td> 
  </tr> 
  <tr> 
   <td>亚像素级角点</td> 
   <td>3.63360</td> 
   <td>18</td> 
  </tr> 
  <tr> 
   <td>SURF角点</td> 
   <td>266.27000</td> 
   <td>151</td> 
  </tr> 
  <tr> 
   <td>Star关键点</td> 
   <td>14.82800</td> 
   <td>56</td> 
  </tr> 
  <tr> 
   <td>FAST角点</td> 
   <td>31.29670</td> 
   <td>159</td> 
  </tr> 
  <tr> 
   <td>SIFT角点</td> 
   <td>287.52310</td> 
   <td>54</td> 
  </tr> 
  <tr> 
   <td>MSER区域</td> 
   <td>40.62970</td> 
   <td>2</td> 
  </tr> 
 </tbody> 
</table>

（图片尺寸：583x301，处理器：AMD ATHLON IIx2 240，内存：DDR3 4G，显卡：GeForce 9500GT，操作系统：Windows 7）

![2010030322142224.png][]

**感谢您耐心看完本文，希望对您有所帮助。**

下一篇文章我们将一起看看如何来跟踪本文讲到的特征点（角点）。

另外，如果需要本文的源代码，请[点击这里下载][Link 1]。

[http_www.cnblogs.com_xrwang_archive_2010_03_03_ImageFeatureDetection.html]: http://www.cnblogs.com/xrwang/archive/2010/03/03/ImageFeatureDetection.html
[ExpandedBlockStart.gif]: /images/20220529/0103cc08fbaf4593ac523a84dafea89b.png
[copycode.gif]: /images/20220529/5c508efc4b414a36a54b91ab96023937.png
[http_www.china-vision.net_blog_user2_15975_archives_2007_804.html]: http://www.china-vision.net/blog/user2/15975/archives/2007/804.html
[http_cvlab.epfl.ch_vlepetit]: http://cvlab.epfl.ch/~vlepetit/
[http_www.cs.ubc.ca_lowe_keypoints]: http://www.cs.ubc.ca/~lowe/keypoints/
[http_www.vlfeat.org]: http://www.vlfeat.org/
[2010030322142224.png]: /images/20220529/375bec4078ec4ad38997c12620fa7132.png
[Link 1]: http://files.cnblogs.com/xrwang/%E7%89%B9%E5%BE%81%E6%A3%80%E6%B5%8B.rar