武汉理工大学数字图像处理英文课件 Chapter2（5）.pptVIP

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武汉理工大学数字图像处理英文课件 Chapter2（5）.ppt

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Image Difference with rgb2gray Linear Regression Normal Equations of Least Squares Matlab example Linear Regression Results Central Differences Central Differences Results * gray = imread(kitchen2.tif); rgb = imread(kitchen1.tif); gray = im2double(gray); rgb = im2double(rgb); diff = gray - rgb2gray(rgb); a(1) = max(diff(:)); a(2) = min(diff(:)); rms = std2(diff); a = 0.0935 -0.0390 rms = 0.0111 cdiff = (diff+1)/2; imhist(cdiff); [counts,x] = imhist(cdiff); stem(x(110:150),counts(110:150)); cdx = (diff - a(2))/(a(1)-a(2)); imshow(cdx) N x 3 3 x 1 N x 1 Normally we solve by k = Z\Y in matlab, but N is too large for this to work well. N x 3 3 x 1 N x 1 Now we solve by k = C\b in matlab 3 x 3 3 x 1 3 x 1 (3 x N) (N x 3) (3 x N) (N x 1) covariance matrix gray=imread(kitchen2.tif); gray=im2double(gray); rgb=imread(kitchen1.tif); rgb=im2double(rgb); len=size(rgb); rgbd=reshape(rgb,len(1)*len(2),3); grayd=reshape(gray,len(1)*len(2),1); C=rgbd*rgbd; % Covariance matrix b=rgbd*grayd; k=C\b; diff = gray - k(1)*rgb(:,:,1) - k(2)*rgb(:,:,2) - k(3)*rgb(:,:,3); a = [ max(diff(:)) min(diff(:)) ]; scd = (diff-a(2))/(a(1)-a(2)); imhist(scd); N x 3 3 x 1 N x 1 rgbd is Z grayd is Y C = 1.0e+004 * 6.9239 6.4029 5.6287 6.4029 6.1035 5.3815 5.6287 5.3815 5.1229 b = 1.0e+004 * 6.4717 6.1061 5.4191 k = 0.3277 0.5617 0.1077 a = 0.0789 -0.0369 rms = 0.0105 m = mean(rgbd) m(4) = mean(grayd) for i=1:3 rgbd(:,i) = rgbd(:,i)-m(i); end grayd = grayd - m(4); C=rgbd*rgbd; b=rgbd*grayd; k=C\b; diff = gray - k(1)*rgb(:,:,1) - k(2)*rgb(:,:,2) - k(3)*rgb(:,:,3); a = [max(diff(:)) min(diff(:))]; rms = std2(diff); scd = (diff-a(2))/(a(1)-a(2)); imhist(scd); m = 0.5353 0.5053 0.4753 0.5125 C = 1.0e+004 * 1.8216 1.5864 1.0986 1.5864 1.5566 1.1051 1.0986 1.1051 1.1008 b = 1.0e+004 * 1.5867 1.4947 1.0819 k = 0.3222 0.5653 0.0937 a = 0.0831