我正在尝试设计一种方法来检测此管道的曲率。我尝试应用hough变换，发现了检测到的线，但是它们并不沿着管道表面放置，因此无法平滑以适应beizer曲线。请为这种图像提供一些好的开始方法。[
通过霍夫变换获得的用于检测线条的图像如下
[
我正在使用标准Matlab代码进行概率霍夫变换线检测，该检测生成围绕结构的线段。本质上，管道的形状类似于抛物线，但是对于霍夫抛物线检测，我需要在检测之前提供该点的偏心率。请提出一种很好的方法来找到沿着曲率的离散点，这些离散点可以拟合到抛物线。我已经给opencv和ITK加上了标签，所以如果有可以在这张特定图片上实现的功能，请建议我尝试一下该功能以查看结果。
img = imread('test2.jpg');
rawimg = rgb2gray(img);
[accum, axis_rho, axis_theta, lineprm, lineseg] = Hough_Grd(bwtu, 8, 0.01);
figure(1); imagesc(axis_theta*(180/pi), axis_rho, accum); axis xy;
xlabel('Theta (degree)'); ylabel('Pho (pixels)');
title('Accumulation Array from Hough Transform');
figure(2); imagesc(bwtu); colormap('gray'); axis image;
DrawLines_2Ends(lineseg);
title('Raw Image with Line Segments Detected');
图像的边缘图如下
最佳答案
我建议采用以下方法:
第一步:生成管道的分段。
代表管道的已连接组件应具有被划分为顶部和底部边缘的边缘贴图(请参见随附的图像)。
顶部和底部边缘的大小应相似，并且彼此之间应具有相对恒定的距离。换句话说，它们每像素距离的方差应该很小。
第二阶段-提取曲线
在此阶段，您应该提取曲线的点以执行Beizer拟合。
您可以在顶部边缘或底部边缘执行此计算。
另一个选择是在管道分段的骨架上执行此操作。
结果
管道分段。顶部和底部边缘分别用蓝色和红色标记。
代码
I = mat2gray(imread('ILwH7.jpg'));
im = rgb2gray(I);
%constant values to be used later on
BW_THRESHOLD = 0.64;
MIN_CC_SIZE = 50;
VAR_THRESHOLD = 2;
SIMILAR_SIZE_THRESHOLD = 0.85;
%stage 1 - thresholding & noise cleaning
bwIm = im>BW_THRESHOLD;
bwIm = imfill(bwIm,'holes');
bwIm = imopen(bwIm,strel('disk',1));
CC = bwconncomp(bwIm);
%iterates over the CC list, and searches for the CC which represents the
%pipe
for ii=1:length(CC.PixelIdxList)
%ignore small CC
if(length(CC.PixelIdxList{ii})<50)
continue;
end
%extracts CC edges
ccMask = zeros(size(bwIm));
ccMask(CC.PixelIdxList{ii}) = 1;
ccMaskEdges = edge(ccMask);
%finds connected components in the edges mat(there should be two).
%these are the top and bottom parts of the pipe.
CC2 = bwconncomp(ccMaskEdges);
if length(CC2.PixelIdxList)~=2
continue;
end
%tests that the top and bottom edges has similar sizes
s1 = length(CC2.PixelIdxList{1});
s2 = length(CC2.PixelIdxList{2});
if(min(s1,s2)/max(s1,s2) < SIMILAR_SIZE_THRESHOLD)
continue;
end
%calculate the masks of these two connected compnents
topEdgeMask = false(size(ccMask));
topEdgeMask(CC2.PixelIdxList{1}) = true;
bottomEdgeMask = false(size(ccMask));
bottomEdgeMask(CC2.PixelIdxList{2}) = true;
%tests that the variance of the distances between the points is low
topEdgeDists = bwdist(topEdgeMask);
bottomEdgeDists = bwdist(bottomEdgeMask);
var1 = std(topEdgeDists(bottomEdgeMask));
var2 = std(bottomEdgeDists(topEdgeMask));
%if the variances are low - we have found the CC of the pipe. break!
if(var1<VAR_THRESHOLD && var2<VAR_THRESHOLD)
pipeMask = ccMask;
break;
end
end
%performs median filtering on the top and bottom boundaries.
MEDIAN_SIZE =5;
[topCorveY, topCurveX] = find(topEdgeMask);
topCurveX = medfilt1(topCurveX);
topCurveY = medfilt1(topCurveY);
[bottomCorveY, bottomCurveX] = find(bottomEdgeMask);
bottomCurveX = medfilt1(bottomCurveX);
bottomCorveY = medfilt1(bottomCorveY);
%display results
imshow(pipeMask); hold on;
plot(topCurveX,topCorveY,'.-');
plot(bottomCurveX,bottomCorveY,'.-');
评论