performance - Blob 的集群生长

Question

考虑以下来自 Mathworks 的图像:

我已经用

标记了 Blob

 [L, num]= bwlabel(I);

我如何迭代地连接所有 Blob ，即从一个 Blob 开始并找到离它最近的一个 Blob 。考虑最左边的两个 Blob ，可以从 Blob 的许多点绘制许多线来连接到另一个 Blob ，但最短的一个将通过找到最接近另一个 Blob 的 Blob 像素来获得，在另一个 Blob 中找到一个相似的像素并连接这两个像素。我想以这种方式连接它们。在连接它们之后，这使它们成为一个单一的 Blob ，找到最接近这个新 Blob 的 Blob 将它们连接起来，依此类推，直到整个图像都有一个单一的封闭结构？此外， Blob 并不总是圆形的，它们的形状是随机的。

这里也有人问过类似的问题:

How to find the shortest path between two blobs(contours/closed curves) using MATLAB?
和 http://in.mathworks.com/matlabcentral/newsreader/view_thread/270149

使用 bwdist()，我可以分离两个 blob，并使用蛮力方法通过测试第二个链接中提到的两个 blob 中的所有像素对来找到最短距离，但这需要很长时间。是否有有更好的方法来解决这个问题，以便更快地获得结果吗？

编辑:

这是另一张图片:

所需图片:

Answer 1

方法#1:连接质心点

%// Read image, convert to binary and remove some whitish border across it
im = im2bw(imread('http://i.stack.imgur.com/vUsrl.png'));
BW = im(3:end-2,3:end-2);
figure, imshow(BW), title('Starting/Original Image')

%// Find centroid points for each blob
cpts = reshape(round(struct2array(regionprops(BW,'Centroid'))),2,[])'; %//'

%// Initialize 2 groups- "hungry" & "feeder" groups, naming them as grp1 & grp2
grp1 = []; grp2 = cpts;

%// Initialize the blob index matching IDs
R = 1; C = 1;

while ~isempty(grp2)
    
    %// Get one from Group-2 into Group 1 based on the closest one that was
    %//obtained from the previous iteration. Remove that from Group -2.
    grp1 = [grp1 ; grp2(C,:)];
    grp2(C,:) = [];
    
    %// Find squared distances between those two groups
    sq_distmat = squared_dist(grp1,grp2);
    
    %// Find the IDs minimum one across row and column which would be the
    %IDs for group 1 and 2 respectively, calling them as R and C
    [~,idx] = min(sq_distmat(:));
    [R,C] = ind2sub(size(sq_distmat),idx);
    
    %// Draw the connecting line
    BW = linept(BW, grp1(R,2), grp1(R,1), grp2(C,2), grp2(C,1));
    
end
figure, imshow(BW), title('Final Connected Image')

关联函数-

function sq_distmat = squared_dist(A,B)

[nA,dim] = size(A);
nB = size(B,1);

A_ext = ones(nA,dim*3);
A_ext(:,2:3:end) = -2*A;
A_ext(:,3:3:end) = A.^2;

B_ext = ones(nB,dim*3);
B_ext(:,1:3:end) = B.^2;
B_ext(:,2:3:end) = B;

sq_distmat = A_ext * B_ext.';

return;

动画乐趣-

---

方法#2:连接轮廓点

%// Read image, convert to binary and remove some whitish border across it
im = im2bw(imread('http://i.stack.imgur.com/vUsrl.png'));
BW = im(3:end-2,3:end-2);

%// Find boundary points as a cell array
bpts_cell = bwboundaries(BW);

%// Initialize 2 groups- "hungry" & "feeder" groups, naming them as grp1 & grp2
grp1c = []; grp2c = bpts_cell;

ID = 1;
for iter = 1:numel(bpts_cell)-1
    
    %// Get one from Group-2 into Group 1 based on the closest one that was
    %obtained from the previous iteration. Remove that from Group -2.
    grp1c = [grp1c ; grp2c(ID)];
    grp2c(ID,:) = [];
    grp1 = vertcat(grp1c{:});
    grp2 = vertcat(grp2c{:});
    
    %// Find squared distances between those two groups
    sq_distmat = squared_dist(grp1,grp2);
    
    %// Find the IDs minimum one across row and column which would be the
    %IDs for group 1 and 2 respectively, calling them as R and C
    [~,idx] = min(sq_distmat(:));
    [R,C] = ind2sub(size(sq_distmat),idx);
    
    %// Draw the connecting line
    BW = linept(BW, grp1(R,1), grp1(R,2), grp2(C,1), grp2(C,2));
    
    lens = cellfun('length',grp2c);
    clens = cumsum(lens);    
    ID = find(C<=clens,1);
end

动画乐趣-

带有编辑图像的动画输出 -