问题描述

（Matlab)基于量子粒子群的二维大津图像分割

请教编写基于qpso算法，适应度函数为最大类间方差的图像分割算法，有懂行的请加qq
2893541647,可以交流下，加我时请说csdn

解决方案

类似下面？

 void ImageBinarization(IplImage *src)
85.{   /*对灰度图像二值化，自适应门限threshold*/
86.    int i,j,width,height,step,chanel,threshold;
87.    /*size是图像尺寸，svg是灰度直方图均值，va是方差*/
88.    float size,avg,va,maxVa,p,a,s;
89.    unsigned char *dataSrc;
90.    float histogram[256];
91.
92.    width = src->width;
93.    height = src->height;
94.    dataSrc = (unsigned char *)src->imageData;
95.    step = src->widthStep/sizeof(char);
96.    chanel = src->nChannels;
97.    /*计算直方图并归一化histogram*/
98.    for(i=0; i<256; i++)
99.        histogram[i] = 0;
100.    for(i=0; i<height; i++)
101.        for(j=0; j<width*chanel; j++)
102.        {
103.            histogram[dataSrc[i*step+j]-'0'+48]++;
104.        }
105.        size = width * height;
106.        for(i=0; i<256; i++)
107.            histogram[i] /=size;
108.        /*计算灰度直方图中值和方差*/
109.        avg = 0;
110.        for(i=0; i<256; i++)
111.            avg += i*histogram[i];
112.        va = 0;
113.        for(i=0; i<256; i++)
114.            va += fabs(i*i*histogram[i]-avg*avg);
115.        /*利用加权最大方差求门限*/
116.        threshold = 20;
117.        maxVa = 0;
118.        p = a = s = 0;
119.        for(i=0; i<256; i++)
120.        {
121.            p += histogram[i];
122.            a += i*histogram[i];
123.            s = (avg*p-a)*(avg*p-a)/p/(1-p);
124.            if(s > maxVa)
125.            {
126.                threshold = i;
127.                maxVa = s;
128.            }
129.        }
130.        /*二值化*/
131.        for(i=0; i<height; i++)
132.            for(j=0; j<width*chanel; j++)
133.            {
134.                if(dataSrc[i*step+j] > threshold)
135.                    dataSrc[i*step+j] = 255;
136.                else
137.                    dataSrc[i*step+j] = 0;
138.            }
139.}