如何在MATLAB中将bwlabel合并到附近区域？

Question

在MATLAB中，当我在具有对象的二进制图像上应用bwlabel时，我得到一个地图，用于标识哪些像素属于哪个对象。但是，某些对象彼此接近，应该属于同一个对象。 （例如标记为1,2,3的三个对象，但它们应合并为一个。）我可以以某种方式合并这些区域吗？

Answer 1

我有一些代码可以帮助您入门。这是从MATLAB中获取一个充满文本的样本图像。之后，我所做的是从文本中提取出每个字母的边界框。一旦我这样做，我提取了它们的质心位置，以及与边界框内每个对象相对应的像素。我使用这两个属性来帮助我获得粗略的聚类算法。

以下是我写的算法的步骤：

1. 读入图像并提取每个对象的每个对象的质心和像素位置
2. 创建一个数组，跟踪我们需要访问的对象
3. 虽然我们需要访问至少一个对象：
   • 查找具有此条件的任何对象
   • 将此对象分配给其成员资格仅为此对象的新群集
   • 找到此对象的质心与其他对象的质心之间的距离
   • 如果有欧几里德距离低于某个值的质心，则它与我们在开头声明的对象属于同一个集群。将所有这些对象分配到同一个群集。
4. 重复步骤＃3，直到我们不再需要访问的群集为止。

这是我写的算法：

clear all;
close all;

%//Read in text and extract properties
BW = imread('text.png');
s  = regionprops(BW, 'Centroid', 'PixelList');

%//Create an array that tells us whether or not we have visited this
%//centroid
centroidVisited = false(length(s),1);

%//Create an array that tells us which object belongs to what cluster
membershipList = zeros(length(s),1);

%//List of all centroids for each object
centroidList = reshape([s.Centroid], 2, length(s)).';

%//Initialize cluster count
clusterNumber = 1;

%//Threshold to figure out what is near
distThreshold = 30;

%//Map that gives us which pixel belongs to which cluster
map = zeros(size(BW));

%//If there are any objects we haven't visited...
while (any(centroidVisited == false))

    %//Find one object
    ind = find(centroidVisited == false, 1);

    %//Extract its centroid
    cent = s(ind).Centroid;

    %//Grab pixels where this object is valid
    pixelLocs = s(ind).PixelList;

    %//Find Euclidean distance squared between this centroid to all the
    %//other centroids
    distCentroids = sum(bsxfun(@minus, cent, centroidList).^2, 2);

    %//Find those locations that are lower than the centroid
    %//Also ensure that we filter out those locations that we have already visited
    belowThresh = find(distCentroids < distThreshold*distThreshold & ...
                       centroidVisited == false);

    %//Mark them as visited
    centroidVisited(belowThresh) = true;

    %//Assign their membership number
    membershipList(belowThresh) = clusterNumber;

    %//For each object that belongs to this cluster, mark them with this
    %//membership number
    for k = 1 : length(belowThresh)
        placesToMark = s(belowThresh(k)).PixelList;
        map(sub2ind(size(BW), placesToMark(:,2), placesToMark(:,1))) = ...
           clusterNumber;
    end

    %//For the next cluster 
   clusterNumber = clusterNumber + 1;    
end

%//Create a colour map that is the same size as the number of clusters
colourMap = jet(clusterNumber);

%//This colour map will contain what letters belong to what cluster (colour
%//coded)
colourMapRed = colourMap(:,1);
colourMapGreen = colourMap(:,2);
colourMapBlue = colourMap(:,3);

mapColumn = map(:) + 1;
redPlane = colourMapRed(mapColumn);
greenPlane = colourMapGreen(mapColumn);
bluePlane = colourMapBlue(mapColumn);

redPlane = reshape(redPlane, size(BW,1), size(BW,2));
greenPlane = reshape(greenPlane, size(BW,1), size(BW,2));
bluePlane = reshape(bluePlane, size(BW,1), size(BW,2));

clusterMapColour = cat(3,redPlane, greenPlane, bluePlane);

figure;
subplot(1,2,1);
imshow(BW);
title('Original Image');
subplot(1,2,2);
imshow(clusterMapColour);
title('Clustered Image');

这是我得到的图像：



变量clusterMapColour将说明哪些对象属于彩色编码地图中的哪个群集。您真正关注的是map变量。这与bwlabel的输出非常相似，只是具有相同标签k的对象属于群集k。您必须使用distThreshold变量来获取所需的结果。我选择30作为一种随意的东西，并开始使用。这意味着质心30像素半径的任何内容都被归类为与该质心相同的成员资格。此外，我们会跟踪已经访问过的对象，以便在我们在图像中移动时不再重新分类。

祝你好运！