立體匹配中的NCC，SAD，SSD算法

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;

}