图像形态学 - 自适应阈值（cvAdaptiveThreshold)

自适应阈值：

是一种改进了的阈值技术，其中阈值本身是一个变量，自适应阈值T（x,y）的每个像素点都不同，通过计算像素点周围的b*b区域的加权平均，然后减去一个常数来得到自适应阈值。

 cvAdaptiveThreshold方法：

Provides adaptive thresholding binary image.

void cvAdaptiveThreshold( IplImage* src, IplImage* dst, double max,
CvAdaptiveThreshMethod method, CvThreshType type, double* parameters);
src                          Source image.
dst                          Destination image.
max                        Max parameter, used with the types CV_THRESH_BINARY and CV_THRESH_BINARY_INV only.
method                  Method for the adaptive threshold definition; now CV_STDDEF_ADAPTIVE_THRESH only.
type                        Thresholding type; must be one of
                                  • CV_THRESH_BINARY, 
                                  • CV_THRESH_BINARY_INV, 
                                  • CV_THRESH_TOZERO, 
                                  • CV_THRESH_TOZERO_INV, 
parameters         Pointer to the list of method-specific input parameters. For the method CV_STDDEF_ADAPTIVE_THRESH the value parameters[0] is the size of the                                                     neighborhood: 1-(3x3), 2-(5x5), or 3-(7x7), and parameters[1] is the value of the minimum variance.

#include <highgui.h>#include <math.h>#include <cv.h>IplImage *Igray = 0, *It = 0, *Iat;int main( int argc, char** argv ){if( argc != 7 ){return -1;}//输入命令行double threshold = (double)atof( argv[1] ); //convert string to doubleint threshold_type = atoi( argv[2] ) ? CV_THRESH_BINARY : CV_THRESH_BINARY_INV;int adaptive_method = atoi( argv[3] ) ? CV_ADAPTIVE_THRESH_MEAN_C : CV_ADAPTIVE_THRESH_GAUSSIAN_C;int block_size = atoi( argv[4] );double offset = (double)atof( argv[5] );//加载灰度图if( ( Igray = cvLoadImage( argv[6], CV_LOAD_IMAGE_GRAYSCALE ) ) == 0 ){return -1;}//创建同样大小8位灰度图用于输出It = cvCreateImage( cvSize( Igray -> width, Igray -> height ), IPL_DEPTH_8U, 1 ); //单通道8位灰度图Iat = cvCreateImage( cvSize( Igray -> width, Igray -> height ), IPL_DEPTH_8U, 1 );//阈值化cvThreshold( Igray, It, threshold, 255, threshold_type );cvAdaptiveThreshold( Igray, Iat, 255, adaptive_method, threshold_type, block_size, offset );//命名窗体输出cvNamedWindow( "Raw", 1 );cvNamedWindow( "Threshold", 1 );cvNamedWindow( "Adaptive Threshold", 1 );cvShowImage( "Raw", Igray );cvShowImage( "Threshold", It );cvShowImage( "Adaptive Threshold", Iat );cvWaitKey(0);//回收内存cvReleaseImage( &Igray );cvReleaseImage( &It );cvReleaseImage( &Iat );cvDestroyWindow( "Raw" );cvDestroyWindow( "Threshold" );cvDestroyWindow( "Adaptive Threshold" );return 0;}

/*input*/

在cmd debug目录下输入chapter_5_example_4.exe 15 1 1 71 15 fruits.jpg

 

其中：

【0】=chapter_5_example_4.ex

【1】=15，Threshold

【2】=1，Type

【3】=1，Method

【4】=71，Block Size

【5】=15，Offset 

【6】=原始图像名

/*result*/

raw gray image

cvThreshold处理

cvAdaptiveThreshold处理

结论，自适应阈值效果比较好，可以自动找到图像特征目标的轮廓。

 

转自：http://blog.csdn.net/hitwengqi/article/details/6856768