otsu算法选择使类间方差最大的灰度值为阈值，具有很好的效果 算法具体描述见otsu论文，或冈萨雷斯著名的数字图像处理那本书 这里给出程序流程：
1、计算直方图并归一化histogram 2、计算图像灰度均值avgValue.
3、计算直方图的零阶w[i]和一级矩u[i]
4、计算并找到最大的类间方差（between-class variance）
variance[i]=(avgValue*w[i]-u[i])*(avgValue*w[i]-u[i])/(w[i]*(1-w[i])) 对应此最大方差的灰度值即为要找的阈值 5、用找到的阈值二值化图像
我在代码中做了一些优化，所以算法描述的某些地方跟程序并不一致 otsu代码，先找阈值，继而二值化 // implementation of otsu algorithm // author: onezeros(@yahoo.cn)
// reference: Rafael C. Gonzalez. Digital Image Processing Using MATLAB void cvThresholdOtsu(IplImage* src, IplImage* dst) {
int height=src->height; int width=src->width;
//histogram
float histogram[256]= {0}; for(int i=0; i unsigned char* p=(unsigned char*)src->imageData+src->widthStep*i; for(int j=0; j histogram[*p++]++; } } //normalize histogram int size=height*width; for(int i=0; i<256; i++) { histogram[i]=histogram[i]/size; } //average pixel value float avgValue=0; for(int i=0; i<256; i++) { avgValue+=i*histogram[i]; } int threshold; float maxVariance=0; float w=0,u=0; for(int i=0; i<256; i++) { w+=histogram[i]; u+=i*histogram[i]; float t=avgValue*w-u; float variance=t*t/(w*(1-w)); if(variance>maxVariance) { maxVariance=variance; threshold=i; } } cvThreshold(src,dst,threshold,255,CV_THRESH_BINARY); } // implementation of otsu algorithm // author: onezeros(@yahoo.cn) // reference: Rafael C. Gonzalez. Digital Image Processing Using MATLAB void cvThresholdOtsu(IplImage* src, IplImage* dst) { int height=src->height; int width=src->width; //histogram float histogram[256]= {0}; for(int i=0; i unsigned char* p=(unsigned char*)src->imageData+src->widthStep*i; for(int j=0; j histogram[*p++]++; } } //normalize histogram int size=height*width; for(int i=0; i<256; i++) { histogram[i]=histogram[i]/size; } //average pixel value float avgValue=0; for(int i=0; i<256; i++) { avgValue+=i*histogram[i]; } int threshold; float maxVariance=0; float w=0,u=0; for(int i=0; i<256; i++) { w+=histogram[i]; u+=i*histogram[i]; float t=avgValue*w-u; float variance=t*t/(w*(1-w)); if(variance>maxVariance) { maxVariance=variance; threshold=i; } } cvThreshold(src,dst,threshold,255,CV_THRESH_BINARY); } 更多情况下我们并不需要对每一帧都是用otsu寻找阈值，于是可以先找到阈值，然后用找到的阈值处理后面的图像。下面这个函数重载了上面的，返回值就是阈值。只做了一点改变 // implementation of otsu algorithm // author: onezeros(@yahoo.cn) // reference: Rafael C. Gonzalez. Digital Image Processing Using MATLAB int cvThresholdOtsu(IplImage* src) { int height=src->height; int width=src->width; //histogram float histogram[256]= {0}; for(int i=0; i unsigned char* p=(unsigned char*)src->imageData+src->widthStep*i; for(int j=0; j histogram[*p++]++; } } //normalize histogram int size=height*width; for(int i=0; i<256; i++) { histogram[i]=histogram[i]/size; } //average pixel value float avgValue=0; for(int i=0; i<256; i++) { avgValue+=i*histogram[i]; } int threshold; float maxVariance=0; float w=0,u=0; for(int i=0; i<256; i++) { w+=histogram[i]; u+=i*histogram[i]; float t=avgValue*w-u; float variance=t*t/(w*(1-w)); if(variance>maxVariance) { maxVariance=variance; threshold=i; } } return threshold; } // implementation of otsu algorithm // author: onezeros(@yahoo.cn) // reference: Rafael C. Gonzalez. Digital Image Processing Using MATLAB int cvThresholdOtsu(IplImage* src) { int height=src->height; int width=src->width; //histogram float histogram[256]= {0}; for(int i=0; i unsigned char* p=(unsigned char*)src->imageData+src->widthStep*i; for(int j=0; j histogram[*p++]++; } }