原文地址：http://blog.csdn.net/abcjennifer/article/details/7617883
距离变换和线性滤波器，形态学变换处于平等位置，是图像处理的一种方法，通过使用两遍扫描光栅算法可以快速计算到曲线或点集的距离。
应用：
水平集
快速斜切匹配
图像拼接
图像混合的羽化
临近点配准
方法：
首先对图像进行二值化处理，然后给每个像素赋值为离它最近的背景像素点与其距离（Manhattan距离or欧氏距离），得到distance metric(距离矩阵)，那么离边界越远的点越亮。
实现：
view plain copy Imgori=imread('test.jpg'); I=rgb2gray(Imgori); subplot(2,3,1);imshow(I);title('origin'); Threshold=100; F=I>Threshold;%front %B=I<=Threshold;%background subplot(2,3,4);imshow(F,[]);title('binary'); T=bwdist(F,'chessboard'); subplot(2,3,2);imshow(T,[]);title('chessboard distance transform') %the chessboard distance between (x1,y1) and (x2,y2) is max(│x1 – x2│,│y1 – y2│). T=bwdist(F,'cityblock'); subplot(2,3,3);imshow(T,[]);title('chessboard distance transform') %the cityblock distance between (x1,y1) and (x2,y2) is │x1 – x2│ + │y1 – y2│. T=bwdist(F,'euclidean'); subplot(2,3,5);imshow(T,[]);title('euclidean distance transform') %use Euclidean distance T=bwdist(F,'quasi-euclidean'); subplot(2,3,6);imshow(T,[]);title('quasi-euclidean distance transform') %use quasi-Euclidean distance或者单纯想看这几个距离函数的区别可以用以下code：
view plain copy bw = zeros(200,200); bw(50,50) = 1; bw(50,150) = 1; bw(150,100) = 1; D1 = bwdist(bw,'euclidean'); D2 = bwdist(bw,'cityblock'); D3 = bwdist(bw,'chessboard'); D4 = bwdist(bw,'quasi-euclidean'); figure subplot(2,2,1), subimage(mat2gray(D1)), title('Euclidean') hold on, imcontour(D1) subplot(2,2,2), subimage(mat2gray(D2)), title('City block') hold on, imcontour(D2) subplot(2,2,3), subimage(mat2gray(D3)), title('Chessboard') hold on, imcontour(D3) subplot(2,2,4), subimage(mat2gray(D4)), title('Quasi-Euclidean') hold on, imcontour(D4)