图像特征检测(Image Feature Detection)
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【本文转自】http://www.cnblogs.com/xrwang/archive/2010/03/03/1677515.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：
Sobel算子//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); }
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：
拉普拉斯算子//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); }
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：
Canny算子//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); }
另外，在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：
计算图像的自适应Canny算子阀值/// <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(); }
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：
霍夫变换//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") : ""); }
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：
Harris角点//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); }
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：
ShiTomasi角点//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") : ""); }
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：
亚像素级角点//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") : ""); }
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：
SURF角点//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" : ""); }
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：
Star关键点//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") : ""); }
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：
FAST关键点//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") : ""); }
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/）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：
Lepetit关键点//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") : ""); }
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/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/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：
通过P/Invoke调用vlfeat函数来进行SIFT检测//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") : ""); }
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：
通过dotnet封装的SiftDetector类来进行SIFT检测//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") : ""); }
对vlfeat库中的SIFT部分封装代码如下所示：
定义SiftDetector类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 }}
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：
MSER（区域）特征检测//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() : ""); }
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：
（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）
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。
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