How to separate overlapping blobs? [duplicate] - matlab

Question

I'm using the function regionprops to detect the number of trees on a image taked by drone.
First I removed the ground using Blue NDVI:
Image with threshold:
Then I used the function regionprops to detect the number of trees on image:
But there are a problem on region 15, because all trees on that region are connected and it detects as one tree.
I tried to separate the trees on that region using Watershed Segmentation, but its not working:
Am I doing this the wrong way?
Is there a better method to separate the trees?
If anyone can help me with this problem I will appreciate. Here is the region 15 without the ground:
If it helps, here is the Gradient Magnitude image:

Answer 1

It has been some time since this question was asked. I hope it is not too late for an answer. I see a general problem of using watershed segmentation in similar questions. Sometimes the objects are apart, not touching each other like in this example . In such cases, only blurring the image is enough to use watershed segmentation. Sometimes the objects are located closely and touch each other, thus the boundaries of objects are not clear like in this example. In such cases, using distance transform-->blur-->watershed helps. In this question, the logical approach should be using distance transform. However, this time the boundaries are not clear due to shadows on and nearby the trees. In such cases, it is good to use any information that helps to separate the objects as in here or emphasise objects itself.
In this question, I suggest using colour information to emphasise tree pixels.
Here are the MATLAB codes and results.
im=imread('https://i.stack.imgur.com/aBHUL.jpg');
im=im(58:500,86:585,:);
imOrig=im;
%% Emphasize trees
im=double(im);
r=im(:,:,1);
g=im(:,:,2);
b=im(:,:,3);
tmp=((g-r)./(r-b));
figure
subplot(121);imagesc(tmp),axis image;colorbar
subplot(122);imagesc(tmp>0),axis image;colorbar
%% Transforms
% Distance transform
im_dist=bwdist(tmp<0);
% Blur
sigma=10;
kernel = fspecial('gaussian',4*sigma+1,sigma);
im_blured=imfilter(im_dist,kernel,'symmetric');
figure
subplot(121);imagesc(im_dist),axis image;colorbar
subplot(122);imagesc(im_blured),axis image;colorbar
% Watershed
L = watershed(max(im_blured(:))-im_blured);
[x,y]=find(L==0);
figure
subplot(121);
imagesc(imOrig),axis image
hold on, plot(y,x,'r.','MarkerSize',3)
%% Local thresholding
trees=zeros(size(im_dist));
centers= [];
for i=1:max(L(:))
ind=find(L==i & im_blured>1);
mask=L==i;
[thr,metric] =multithresh(g(ind),1);
trees(ind)=g(ind)>thr*1;
trees_individual=trees*0;
trees_individual(ind)=g(ind)>thr*1;
s=regionprops(trees_individual,'Centroid');
centers=[centers; cat(1,[],s.Centroid)];
end
subplot(122);
imagesc(trees),axis image
hold on, plot(y,x,'r.','MarkerSize',3)
subplot(121);
hold on, plot(centers(:,1),centers(:,2),'k^','MarkerFaceColor','r','MarkerSize',8)

Answer 2

You could try out a marker-based watershed. Vanilla watershed transforms never work out of the box in my experience. One way to perform one would be to first create a distance map of the segmented area by using imdist(). Then you could suppress local maxima by calling imhmax(). Then calling watershed() will usually perform noticeably better.
Here's a sample script on how to do it:
bwTrees = imopen(bwTrees, strel('disk', 10));
%stabilize the borders to lessen oversegmentation
distTrees = -bwDist(~bwTrees); %Distance transform
distTrees(~bwTrees) = -Inf; %set background to -Inf
distTrees = imhmin(distTrees, 3); %suppress local minima
basins = watershed(distTrees);
ridges = basins == 0;
segmentedTrees = bwTrees & ~ridges; %segment
segmentedTrees = imopen(segmentedTrees, strel('disk', 2));
%remove 'segmentation trash' caused by oversegmentation near the borders.
I fiddled around with the parameters for ~10min but got fairly poor results:
You'd need to pour work into this. Mostly in pre- and post-processing via the morphology. More curvature would help the segmentation, if you could lower the sensitivity of the segmentation in the first part. The size of the h-minima transform is also an paramater of interest. You can probably get adequate results this way.
Probably a better approach would come from the world of clustering techniques. If you have or can find a way to estimate the number of trees in the forest you should be able to use traditional clustering methods to segment out the trees. A Gaussian mixture model or a k-means with k-trees would probably work much better than a marker based watershed if you get even nearly the right amount of trees. Normally I'd estimate the number of trees based on the number of suppressed maxima on a h-maxima transform, but your labels might be a bit too sausagey for that. It's worth a try though.