Question

我的第一篇帖子=）我在MATLAB中遇到了一个看似简单的计算问题。我有一个1000x1000的0和1的矩阵。 1s在矩阵的对角线上聚集成粗线，我需要测量这些线簇的梯度。（从SW到NE的粗白线）。到目前为止我所做的是在每个簇上放置一个标尺并提取线的点。然而，这不是一个解决方案，因为我有2000个矩阵来分析。

enter image description here

阅读渐变 -

enter image description here

问题：

我不能适应渐变，因为有多个线簇。
我尝试使用imclose去除杂散点，但它并没有帮助我隔离每个集群
我尝试使用边缘检测和Hough 变换，但它们都没有帮助我隔离集群。

提前非常感谢。如果我的问题不清楚，请告诉我=）

Answer 1

<强>代码

%%// Select approach
%%//   1. Gradient values for all clusters
%%//   2. One dominant gradient value for one image
approach_id = 1;

%%// Threshold to the number of pixels that a blob must have
%%// to be declared as a cluster
thresh = 850;

%%// Image scaling factor
img_scale = 0.2; %%// 0.2 seemed to work for the sample

img = imread(image_filenpath);
bw1 = im2bw(img,0.3); %%// 0.3 as threshold-level worked for sample image
bw2 = medfilt2(bw1,[5 5]); %%// 5x5 as denoising window worked

[L, num] = bwlabel(bw2, 8);
counts = sum(bsxfun(@eq,L(:),1:num));


switch approach_id

    case 1
        count1 = 1;
        for k = 1:num
            if counts(k)>thresh
                bw5 = imresize(L==k,img_scale);
                gradient1(count1) = gradval(bw5);
                count1 = count1+1;
            end
        end

    case 2
        bw4 = false(size(bw1));
        for k = 1:num
            if counts(k)>thresh
                bw4 = bw4 | L==k;
            end
        end
        %%// At this point we have a cleaned-up binary image of the input
        bw5 = imresize(bw4,img_scale);
        gradient1 = gradval(bw5);

end

%%// gradient1 is what you need

相关功能

function gradient_value = gradval(BW)

angles = 45:-1:0;

for iter = 1:numel(angles)
    BWr = imrotate(BW,angles(iter));
    t1(iter) = max(sum(BWr,1));
end
[~,ind] = max(t1);
gradient_value = tand(90 - angles(ind));

return;

使用样本图像的群集渐变值输出

gradient1 =

    1.6643    1.9626    2.0503    2.0503

请注意，群集是根据MATLAB中使用的列主索引进行排序的。

检测粗线簇并测量梯度

1 个答案: