其主要任务是消除复杂的背景,并在MATLAB中从被遮挡的叶片图像中提取目标叶。为了消除背景,我已经应用了K-均值聚类算法。现在的主要任务是利用分水岭分割算法将叶从被遮挡的叶中分割出来。我无法为每一片叶子找到完美的片段。请帮帮我。我已经上传了样本图像和分水岭分割代码。
原始图像

基于K均值聚类算法的图像去除后的分水岭分割

我希望主中间的叶子是一个单独的片段,这样我就可以提取它。
我已经给出了下面的分水岭分段代码
function wateralgo(img)
F=imread(img);
F=im2double(F);
%Converting RGB image to Intensity Image
r=F(:,:,1);
g=F(:,:,2);
b=F(:,:,3);
I=(r+g+b)/3;
imshow(I);
%Applying Gradient
hy = fspecial('sobel');
hx = hy';
Iy = imfilter(double(I), hy, 'replicate');
Ix = imfilter(double(I), hx, 'replicate');
gradmag = sqrt(Ix.^2 + Iy.^2);
figure, imshow(gradmag,[]), title('Gradient magnitude (gradmag)');
L = watershed(gradmag);
Lrgb = label2rgb(L);
figure, imshow(Lrgb), title('Watershed transform of gradient magnitude (Lrgb)');
se = strel('disk',20);
Io = imopen(I, se);
figure, imshow(Io), title('Opening (Io)');
Ie = imerode(I, se);
Iobr = imreconstruct(Ie, I);
figure, imshow(Iobr), title('Opening-by-reconstruction (Iobr)');
Ioc = imclose(Io, se);
figure, imshow(Ioc), title('Opening-closing (Ioc)');
Iobrd = imdilate(Iobr, se);
Iobrcbr = imreconstruct(imcomplement(Iobrd), imcomplement(Iobr));
Iobrcbr = imcomplement(Iobrcbr);
figure, imshow(Iobrcbr), title('Opening-closing by reconstruction (Iobrcbr)');
fgm = imregionalmin(Iobrcbr);
figure, imshow(fgm), title('Regional maxima of opening-closing by reconstruction (fgm)');
I2 = I;
I2(fgm) = 255;
figure, imshow(I2), title('Regional maxima superimposed on original image (I2)');
se2 = strel(ones(7,7));
fgm2 = imclose(fgm, se2);
fgm3 = imerode(fgm2, se2);
fgm4 = bwareaopen(fgm3, 20);
I3 = I;
I3(fgm4) = 255;
figure, imshow(I3), title('Modified regional maxima superimposed on original image (fgm4)');
bw = im2bw(Iobrcbr, graythresh(Iobrcbr));
figure, imshow(bw), title('Thresholded opening-closing by reconstruction (bw)');
D = bwdist(bw);
DL = watershed(D);
bgm = DL == 0;
figure, imshow(bgm), title('Watershed ridge lines (bgm)');
gradmag2 = imimposemin(gradmag, bgm | fgm4);
L = watershed(gradmag2);
I4 = I;
I4(imdilate(L == 0, ones(3, 3)) | bgm | fgm4) = 255;
figure, imshow(I4), title('Markers and object boundaries superimposed on original image (I4)');
Lrgb = label2rgb(L, 'jet', 'w', 'shuffle');
figure, imshow(Lrgb), title('Colored watershed label matrix (Lrgb)');
figure, imshow(I), hold on
himage = imshow(Lrgb);
set(himage, 'AlphaData', 0.3);
title('Lrgb superimposed transparently on original image');
end发布于 2012-05-08 08:30:21
我认为你应该尝试前景提取算法,而不是一般的分割。这样的算法之一是GrabCut。另一件有用的事情是,在试图提取前景对象之前,在图像表示中实现一定程度的光照差异。这样做的一种方法是在冲色空间中工作。
发布于 2012-05-30 15:25:18
如果用户的任何交互都是可能的,那么使用GrabCut (如@Victor May所提到的)或更基本的交互式图形切割,您的分割效果会更好。
否则,自动分割是很难完善的各种图像。也许您可以尝试一些后处理,根据相似性度量(或者基于两个片段之间的梯度强度?)对相邻区域进行比较和合并。
https://stackoverflow.com/questions/10284331
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