数字图像处理图像特征提取

1、会计学1数字图像处理图像特征提取数字图像处理图像特征提取23颜色形状纹理；n而对于灰度信息处理，主要讲述直方图技术。45HSV色彩空间YUV色彩空间RGB色彩空间Lab色彩空间XYZ色彩空间678原始彩色图像红色分量图像绿色分量图像蓝色分量图像910111213原始彩色图像色调分量图像饱和度分量图像亮度分量图像141516原始彩色图像Y（亮度）分量图像U（色度）分量图像V（浓度）分量图像1718原始彩色图像X分量图像Y分量图像Z分量图像192021原始彩色图像L分量图像a分量图像b分量图像2223CTArray CImageProcessing:Histogram_of_gray_image(

2、 const CTMatrix& gray_image )const long dimension = 256; / 直方图维数CTArray histogram( dimension ); / 构造直方图for( int index = 0; index dimension; index + ) / 直方图遍历histogram index = 0; long image_height = gray_image.Get_height(); / 图像高度long image_width = gray_image.Get_width(); / 图像宽度for( int row = 0; row

3、image_height; row + )for( int column = 0; column image_width; column + ) histogram gray_imagerowcolumn +; return histogram; / 返回直方图242526272829CTMatrix CImageProcessing:Histogram_equalization( const CTMatrix& gray_image, long equal_scale )long image_height = gray_image.Get_height();long image_width

4、= gray_image.Get_width();CTMatrix result_image( image_height, image_width );CTArray histogram = Normalize_histogram( Histogram_of_gray_image( gray_image ) );for( int index = 1; index histogram.GetDimension(); index + )histogram index += histogram index - 1 ;for( int index = 0; index histogram.GetDim

5、ension(); index + )histogram index = long( long( histogram index * equal_scale ) * ( 256.0 / double( equal_scale ) ) );histogram index = max( 0, histogram index );histogram index = min( 255, histogram index );for( int row = 0; row image_height; row + )for( int column = 0; column image_width; column

6、+ )result_image row column = BYTE( histogram gray_image row column );return result_image;30原始图像3132333435n部特征（如直线段、圆弧、角点、高曲率点等），以解决遮挡问题。3637Hu不变矩傅里叶描述子Hough变换Zernike不变矩Radon变换383940414243444546474849505152535455CTArray CImageProcessing:Position_from_binary_image( const CTMatrix& binary_image, BlackW

7、hite object_color )long number_of_objects = 0;long image_height = binary_image.Get_height();long image_width = binary_image.Get_width();for( int row = 0; row image_height; row + )for( int column = 0; column image_width; column + )if( binary_image row column = object_color )number_of_objects +;CTArra

8、y array_of_positions( number_of_objects );number_of_objects = 0;for( int row = 0; row image_height; row + )for( int column = 0; column image_width; column + )if( binary_image row column = object_color )array_of_positions number_of_objects .m_re = row;array_of_positions number_of_objects .m_im = colu

9、mn;number_of_objects +;return array_of_positions;56CTMatrix CImageProcessing:Binary_image_from_position( const CTArray& array_of_positions, long image_height, long image_width, BlackWhite object_color )CTMatrix binary_image( image_height, image_width );for( int row = 0; row image_height; row + )for(

10、 int column = 0; column image_width; column + )binary_image row column = ( object_color = White ? Black : White );long dimension = array_of_positions.GetDimension();for( int index = 0; index dimension; index + )long row = long( array_of_positions index .m_re );long column = long( array_of_positions index .m_im );if( binary_image.Is_point_valid( CImagePoint(