有很多介紹Gabor的文章，Gabor是一種紋理特征提取，和灰度共生矩陣類(lèi)似

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利用Gabor濾波對(duì)圖像進(jìn)行分割

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%gabor 用于圖像分割 %%讀并顯示圖像 A = imread('kobi.png'); A = imresize(A,0.25); Agray = rgb2gray(A); figure imshow(A) title('原圖輸入')%%Gabor 濾波器的設(shè)計(jì)陣列 %設(shè)計(jì)一系列調(diào)諧到不同頻率和方向的Gabor濾波器 imageSize = size(A); numRows = imageSize(1); numCols = imageSize(2);wavelengthMin = 4/sqrt(2); %采樣波長(zhǎng) wavelengthMax = hypot(numRows,numCols); n = floor(log2(wavelengthMax/wavelengthMin)); wavelength = 2.^(0:(n-2)) * wavelengthMin;deltaTheta = 45; orientation = 0:deltaTheta:(180-deltaTheta);g = gabor(wavelength,orientation);gabormag = imgaborfilt(Agray,g); %%將 Gabor 幅度圖像后處理為 Gabor 特征 %平滑Gabor幅度 for i = 1:length(g)sigma = 0.5*g(i).Wavelength;K = 3;gabormag(:,:,i) = imgaussfilt(gabormag(:,:,i),K*sigma); end %用于分類(lèi)時(shí)最好加上X Y信息 X = 1:numCols; Y = 1:numRows; [X,Y] = meshgrid(X,Y); featureSet = cat(3,gabormag,X); featureSet = cat(3,featureSet,Y); %每個(gè)像素有24個(gè)Gabor特征和兩個(gè)空間特征 numPoints = numRows*numCols; X = reshape(featureSet,numRows*numCols,[]); %對(duì)數(shù)據(jù)進(jìn)行歸一化為零均值，單位方差 X = bsxfun(@minus, X, mean(X)); X = bsxfun(@rdivide,X,std(X)); %將26個(gè)特征轉(zhuǎn)化為二維圖像的像素 coeff = pca(X); feature2DImage = reshape(X*coeff(:,1),numRows,numCols); figure imshow(feature2DImage,[]);title('PCA圖像');%利用kmeans對(duì)Gabor紋理特征進(jìn)行分類(lèi) L = kmeans(X,2,'Replicates',5); L = reshape(L,[numRows numCols]); figure imshow(label2rgb(L));title('分割mask');Aseg1 = zeros(size(A),'like',A); Aseg2 = zeros(size(A),'like',A); BW = L == 2; BW = repmat(BW,[1 1 3]); Aseg1(BW) = A(BW); Aseg2(~BW) = A(~BW); figure imshowpair(Aseg1,Aseg2,'montage');

原圖

pca特征圖

kmean分割Gabor特征圖像mask

分割圖像結(jié)果

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