原创 图像阈值分割方法

OTSU（最大类间差分）

44     IplImage* imgOtsu = cvCreateImage(cvGetSize(imgGrey),IPL_DEPTH_8U,1);
45     cvCopyImage(srcImgGrey,imgOtsu);
46     int thre2;
47     thre2 = otsu2(imgOtsu);
48     cout<<"The Threshold of this Image in Otsu is:"<<thre2<<endl;//输出显示阀值
49     cvThreshold(imgOtsu,imgOtsu,thre2,255,CV_THRESH_BINARY);  //  二值化    
50     cvNamedWindow("imgOtsu", CV_WINDOW_AUTOSIZE );
51     cvShowImage( "imgOtsu", imgOtsu);//显示图像    
52     cvReleaseImage(&imgOtsu);

////////////////////////////////////////////////////////////////

1 /*======================================================================*/
 2 /* OTSU global thresholding routine */
 3 /* takes a 2D unsigned char array pointer, number of rows, and */
 4 /* number of cols in the array. returns the value of the threshold */
 5 /*parameter: 
 6 *image --- buffer for image
 7 rows, cols --- size of image
 8 x0, y0, dx, dy --- region of vector used for computing threshold
 9 vvv --- debug option, is 0, no debug information outputed
10 */
11 /*
12 OTSU 算法可以说是自适应计算单阈值（用来转换灰度图像为二值图像）的简单高效方法。
13 下面的代码最早由 Ryan Dibble提供，此后经过多人Joerg.Schulenburg, R.Z.Liu 等修改，补正。
14 算法对输入的灰度图像的直方图进行分析，将直方图分成两个部分，使得两部分之间的距离最大。
15 划分点就是求得的阈值。
16 */
17 /*======================================================================*/
18 int otsu (unsigned char *image, int rows, int cols, int x0, int y0, int dx, int dy, int vvv)
19 {
20     
21     unsigned char *np; // 图像指针
22     int thresholdValue=1; // 阈值
23     int ihist[256]; // 图像直方图，256个点
24     
25     int i, j, k; // various counters
26     int n, n1, n2, gmin, gmax;
27     double m1, m2, sum, csum, fmax, sb;
28     
29     // 对直方图置零
30     memset(ihist, 0, sizeof(ihist));
31     
32     gmin=255; gmax=0;
33     // 生成直方图
34     for (i = y0 + 1; i < y0 + dy - 1; i++) 
35     {
36         np = (unsigned char*)image[i*cols+x0+1];
37         for (j = x0 + 1; j < x0 + dx - 1; j++)
38         {
39             ihist[*np]++;
40             if(*np > gmax) gmax=*np;
41             if(*np < gmin) gmin=*np;
42             np++; /* next pixel */
43         }
44     }
45     
46     // set up everything
47     sum = csum = 0.0;
48     n = 0;
49     
50     for (k = 0; k <= 255; k++) 
51     {
52         sum += (double) k * (double) ihist[k]; /* x*f(x) 质量矩*/
53         n += ihist[k]; /* f(x) 质量 */
54     }
55     
56     if (!n) 
57     {
58         // if n has no value, there is problems...
59         fprintf (stderr, "NOT NORMAL thresholdValue = 160\n");
60         return (160);
61     }
62     
63     // do the otsu global thresholding method
64     fmax = -1.0;
65     n1 = 0;
66     for (k = 0; k < 255; k++)
67     {
68         n1 += ihist[k];
69         if (!n1) 
70         { 
71             continue; 
72         }
73         n2 = n - n1;
74         if (n2 == 0)
75         { 
76             break; 
77         }
78         csum += (double) k *ihist[k];
79         m1 = csum / n1;
80         m2 = (sum - csum) / n2;
81         sb = (double) n1 *(double) n2 *(m1 - m2) * (m1 - m2);
82         /* bbg: note: can be optimized. */
83         if (sb > fmax) 
84         {
85             fmax = sb;
86             thresholdValue = k;
87         }
88     }
89     
90     // at this point we have our thresholding value
91     
92     // debug code to display thresholding values
93     if ( vvv & 1 )
94         fprintf(stderr,"# OTSU: thresholdValue = %d gmin=%d gmax=%d\n",
95         thresholdValue, gmin, gmax);
96     
97     return(thresholdValue);
98 } 

////////////////////////////////////////////////////////////////////////////////

6.

/*上下阀值法：利用正态分布求可信区间*/