立體匹配中的NCC，SAD，SSD算法

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http://www.cnblogs.com/cvlabs/archive/2010/01/13/1646902.html

Normalized Cross correlation (NCC)

NCC(u,v) = [(wl - w)/(|wl - w|)]*[(wr - w)/(|wr - w|)] 選擇最大值

Sum of Squared Defferences (SSD)

SSD(u,v) = Sum{[Left(u,v) - Right(u,v)] * [Left(u,v) - Right(u,v)]} 選擇最大值

Sum of Absolute Defferences (SAD)

SAD(u,v) = Sum{|Left(u,v) - Right(u,v)|} 選擇最小值

先說說SAD算法的基本流程：

1.構造一個小窗口，類似與卷積核。

2.用窗口覆蓋左邊的圖像，選擇出窗口覆蓋區域內的所有像素點。

3.同樣用窗口覆蓋右邊的圖像並選擇出覆蓋區域的像素點。

4.左邊覆蓋區域減去右邊覆蓋區域，並求出所有像素點差的絕對值的和。

5.移動右邊圖像的窗口，重復3，4的動作。（這裡有個搜索范圍，超過這個范圍跳出）

6.找到這個范圍內SAD值最小的窗口，即找到了左邊圖像的最佳匹配的像素塊。

OpenCV代碼示范SAD:

代碼Code highlighting produced by Actipro CodeHighlighter (freeware)http://www.CodeHighlighter.com/--> 1 IplImage* generateDisparityImage(IplImage* greyLeftImg32,                          IplImage* greyRightImg32,                           int windowSize,int DSR){      int offset=floor((double)windowSize/2);     int height=greyLeftImg32->height;    int width=greyLeftImg32->width;     double* localSAD=new double[DSR];//DSR即搜索范圍     int x=0, y=0,d=0,m=0;    int N=windowSize;                  IplImage* winImg=cvCreateImage(cvSize(N,N),32,1);//mySubImage(greyLeftImg32,cvRect(0,0,N,N));        IplImage* disparity=cvCreateImage(cvSize(width,height),8,1);//or IPL_DEPTH_8U    BwImage imgA(disparity);        for (y=0;y<height;y++){        for (x=0;x<width;x++){          imgA[y][x]=0;       }    }          CvScalar sum;    //CvScalar s2;    for (y=0;y<height-N;y++){ //height-N       for (x=0;x<width-N;x++){//width-N           cvSetImageROI(greyLeftImg32, cvRect(x,y,N,N));          d=0;                    //initialise localSAD           for (m=0;m<DSR;m++){localSAD[m]=0;}                       //start matching           do{              if (x-d>=0){                 cvSetImageROI(greyRightImg32, cvRect(x-d,y,N,N));              }else{                break;             }               cvAbsDiff(greyLeftImg32,greyRightImg32,winImg);//absolute difference             sum=cvSum(winImg);//sum             localSAD[d]=sum.val[0];//0 means single channel              cvResetImageROI(greyRightImg32);              d++;          }while(d<=DSR);                    //to find the best d and store           imgA[y+offset][x+offset]=getMaxMin(localSAD,DSR,0)*16; //0 means return minimum index          cvResetImageROI(greyLeftImg32);       }//x        if (y%10==0)cout<<"row="<<y<<" of "<<height<<endl;      }//y        cvReleaseImage(&winImg);     //cvReleaseImage(&rightWinImg);              return disparity;  }