直方圖反向投影式通過給定的直方圖信息，在圖像找到相應的像素分布區域，opencv提供兩種算法，一個是基於像素的，一個是基於塊的。
使用方法不寫了，可以參考一下幾個網站：
直方圖反向投影參考1
直方圖參考2
測試例子1：灰度直方圖反向投影
灰度直方圖反向投影
IplImage * image= cvLoadImage("22.jpg");
IplImage * image2= cvLoadImage("2.jpg");
int hist_size=256;
float range[] = {0,255};
float* ranges[]={range};
IplImage* gray_plane = cvCreateImage(cvGetSize(image),8,1);
cvCvtColor(image,gray_plane,CV_BGR2GRAY);
CvHistogram* gray_hist = cvCreateHist(1,&hist_size,CV_HIST_ARRAY,ranges,1);
cvCalcHist(&gray_plane,gray_hist,0,0);
//cvNormalizeHist(gray_hist,1.0);
IplImage* gray_plane2 = cvCreateImage(cvGetSize(image2),8,1);
cvCvtColor(image2,gray_plane2,CV_BGR2GRAY);
//CvHistogram* gray_hist2 = cvCreateHist(1,&hist_size,CV_HIST_ARRAY,ranges,1);
//cvCalcHist(&gray_plane2,gray_hist2,0,0);
//cvNormalizeHist(gray_hist2,1.0);
IplImage* dst = cvCreateImage(cvGetSize(gray_plane2),IPL_DEPTH_8U,1);
cvCalcBackProject(&gray_plane2, dst ,gray_hist);
cvEqualizeHist(dst,dst);
//產生的圖像太暗，做了一些直方圖均衡
cvNamedWindow( "dst");
cvShowImage("dst",dst);
cvNamedWindow( "src");
cvShowImage( "src", image2 );
cvNamedWindow( "templ");
cvShowImage( "templ", image );
cvWaitKey();
效果圖：
第一個圖為源圖像，中間的那個小圖像是產生用於反向投影的直方圖的圖像，最后的用直方圖均衡化后的結果圖像，可以看到，蘋果的像素位置幾被找到了。
測試例子2：彩色直方圖反向投影測試
彩色圖像直方圖反向投影
IplImage*src= cvLoadImage("myhand2.jpg", 1);
IplImage*templ=cvLoadImage("myhand3.jpg",1);
cvNamedWindow( "Source" );
cvShowImage( "Source", src );
IplImage* h_plane2 = cvCreateImage( cvGetSize(src), 8, 1 );
IplImage* s_plane2 = cvCreateImage( cvGetSize(src), 8, 1 );
IplImage* v_plane2 = cvCreateImage( cvGetSize(src), 8, 1);
IplImage* planes2[] = { h_plane2, s_plane2,v_plane2 };
IplImage* hsv2 = cvCreateImage( cvGetSize(src), 8, 3 );
cvCvtColor( src, hsv2, CV_BGR2HSV );
cvSplit( hsv2, h_plane2, s_plane2, v_plane2, 0 );
printf("h%d",h_plane2->widthStep);
printf("s%d",h_plane2->widthStep);
printf("v%d",h_plane2->widthStep);
IplImage* h_plane = cvCreateImage( cvGetSize(templ), 8, 1 );
IplImage* s_plane = cvCreateImage( cvGetSize(templ), 8, 1 );
IplImage* v_plane = cvCreateImage( cvGetSize(templ), 8, 1);
IplImage* planes[] = { h_plane, s_plane,v_plane };
IplImage* hsv = cvCreateImage( cvGetSize(templ), 8, 3 );
cvCvtColor( templ, hsv, CV_BGR2HSV );
cvSplit( hsv, h_plane, s_plane, v_plane, 0 );
printf("h%d\n",h_plane->widthStep);
printf("s%d\n",s_plane->widthStep);
printf("v%d\n",v_plane->widthStep);
int h_bins = 16, s_bins = 16,v_bins=16;
int hist_size[] = {h_bins, s_bins,v_bins};
float h_ranges[] = {0,255};
float s_ranges[] = {0,255};
float v_ranges[] = {0,255};
float* ranges[] = { h_ranges, s_ranges,v_ranges};
CvHistogram* hist;
hist = cvCreateHist( 3, hist_size, CV_HIST_ARRAY, ranges, 1 );
cvCalcHist( planes, hist, 0, 0 );
//1.double a=1.f;
//2.cvNormalizeHist(hist,a);
//templ's hist is just calculate
IplImage*back_project=cvCreateImage(cvGetSize(src),8,1);//!!歸一，把改成，就彈出對話框，說planes的steps不是一致的！
cvZero(back_project); //但是我去掉歸一，改成就可以顯示
//NOW we begin calculate back project
cvCalcBackProject(planes2,back_project,hist);
cvNamedWindow( "back_project" );
cvShowImage( "back_project", back_project );
cvWaitKey(0);
測試結果：
手的膚色位置基本找到了，但是有一個問題，在做直方圖反向的時候，直方圖分級是16等分，並不是256等分，下圖是32等分和8等分的圖像效果：
32等分 
8等分
程序里面使用了SHV分量，也算是膚色檢測的一個實例，里面的顏色區分很明顯，所有采用大一點的區域統計，能更好的找到膚色的位置，如果采用很細的顏色區分，光照的影響也會考慮進去了。
測試例子3：基於塊的直方圖投影
這種方法速度很慢，模版圖像別弄的太大了。
基於塊的圖像直方圖反向投影
IplImage*src= cvLoadImage("2.jpg", 1);
IplImage*templ=cvLoadImage("22.jpg",1);
cvNamedWindow( "Source" );
cvShowImage( "Source", src );
IplImage* h_plane2 = cvCreateImage( cvGetSize(src), 8, 1 );
IplImage* s_plane2 = cvCreateImage( cvGetSize(src), 8, 1 );
IplImage* v_plane2 = cvCreateImage( cvGetSize(src), 8, 1);
IplImage* planes2[] = { h_plane2, s_plane2,v_plane2 };
IplImage* hsv2 = cvCreateImage( cvGetSize(src), 8, 3 );
cvCvtColor( src, hsv2, CV_BGR2HSV );
cvSplit( hsv2, h_plane2, s_plane2, v_plane2, 0 );
printf("h%d",h_plane2->widthStep);
printf("s%d",h_plane2->widthStep);
printf("v%d",h_plane2->widthStep);
IplImage* h_plane = cvCreateImage( cvGetSize(templ), 8, 1 );
IplImage* s_plane = cvCreateImage( cvGetSize(templ), 8, 1 );
IplImage* v_plane = cvCreateImage( cvGetSize(templ), 8, 1);
IplImage* planes[] = { h_plane, s_plane,v_plane };
IplImage* hsv = cvCreateImage( cvGetSize(templ), 8, 3 );
cvCvtColor( templ, hsv, CV_BGR2HSV );
cvSplit( hsv, h_plane, s_plane, v_plane, 0 );
printf("h%d\n",h_plane->widthStep);
printf("s%d\n",s_plane->widthStep);
printf("v%d\n",v_plane->widthStep);
int h_bins = 16, s_bins = 16,v_bins=16;
int hist_size[] = {h_bins, s_bins,v_bins};
float h_ranges[] = {0,255};
float s_ranges[] = {0,255};
float v_ranges[] = {0,255};
float* ranges[] = { h_ranges, s_ranges,v_ranges};
CvHistogram* hist;
hist = cvCreateHist( 3, hist_size, CV_HIST_ARRAY, ranges, 1 );
cvCalcHist( planes, hist, 0, 0 );
CvSize temp ;
temp.height = src->height - templ->height + 1;
temp.width = src->width - templ->width + 1;
IplImage*back_project=cvCreateImage(temp,IPL_DEPTH_32F,1);//!!歸一，把改成，就彈出對話框，說planes的steps不是一致的！
cvZero(back_project); //但是我去掉歸一，改成就可以顯示
cvCalcBackProjectPatch(planes2, back_project, cvGetSize(templ), hist,CV_COMP_INTERSECT ,1);
cvNamedWindow( "back_project" );
cvShowImage( "back_project", back_project );
cvWaitKey(0);
測試圖像：
當模版圖像小雨目標的時候，作為區域檢測器，測試如下：可以找到手區域
當模版等於目標的時候，測試如下：輸出圖像，較亮的部分就是人的頭部大致位置
基於塊的反向，速度太慢了。
