距離變換和線性濾波器，形態學變換處於平等位置，是圖像處理的一種方法，通過使用兩遍掃描光柵算法可以快速計算到曲線或點集的距離。
應用：
水平集
快速斜切匹配
圖像拼接
圖像混合的羽化
臨近點配準
方法：
首先對圖像進行二值化處理，然後給每個像素賦值爲離它最近的背景像素點與其距離（Manhattan距離or歐氏距離），得到distance metric(距離矩陣)，那麼離邊界越遠的點越亮。
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實現：
- 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
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或者單純想看這幾個距離函數的區別可以用以下code：
- 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)
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