MatLab - 分割来分离图像中的触摸对象 [英] MatLab - Segmentation to separate touching objects in an image

问题描述

我使用函数regionprops来检测无人机拍摄的图像上的树木数量。

首先，我使用蓝色NDVI移除地面：

然后我使用函数regionprops来检测图像上的树的数量：

但是有一个问题区域15，因为该区域的所有树都连接在一起，并且它检测为一棵树。
我试图使用

我是否以错误的方式行事？
有没有更好的方法来分离树？

如果有人能帮助我解决这个问题，我将不胜感激。这里是没有地面的区域15：

如果有帮助，这里是渐变幅度图像：

解决方案

。我希望答案不迟。我发现在类似问题中使用分水岭分割的一般问题。有时候这些对象是分开的，不会互相接触

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:

解决方案

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)

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