我有一个类似于下面的灰度图像，这是我在一些后处理步骤后获得的(图像 0001)。我想要一个对应于下部亮条底部的向量(如图像 0001b 所示)。我可以使用具有各种阈值的 im2bw 来实现图像 0002 中的矢量(阈值越高，矢量线向上的趋势越高，阈值越低，线向下的趋势越高)..和然后我正在考虑遍历每个向量并在某个增量(可能是 100 像素左右)上测量弧长并选择具有最低弧长的向量……并将 100 像素拉伸(stretch)添加到最终向量中，创建一个类似弗兰肯斯坦的向量使用每个阈值向量中最直的段。我还应该提到，当有多个直/平行向量时，最上面的一个是最合适的。
首先，我应该在这里采用一些更好的策略来找到图像 0001 上的那条线吗？ (这需要很快，所以一些长拟合代码将不起作用)。如果我目前的弗兰肯斯坦怪物解决方案有效，关于如何最好地解决这个问题有什么建议吗？
提前致谢
image=im2bw(image,0.95); %or 0.85, 0.75, 0.65, 0.55
vec=[];
for v=1:x
for x=1:z
if image(c,v)==1
vec(v)=c;
end
end
end
vec=fastsmooth(vec,60,20,1);
最佳答案
这是我最初所做的修改版本。它适用于您的图像。如果你想要亚像素分辨率，你可以使用一些拟合函数来实现一个事件轮廓模型。
files = dir('*.png');
filenames = {files.name};
for ifile=1:length(filenames)
%%
% read image
im0 = double(imread(filenames{ifile}));
%%
% remove background by substracting a convolution with a mask
lobj=100;
convmask = ones(lobj,1)/lobj;
im=im0-conv2(im0,convmask,'same');
im(im<0)=0;
imagesc(im);colormap gray;axis image;
%%
% use canny edge filter, alowing extremely weak edge to exist
bw=edge(im,'canny',[0.01,0.3]);
% use close operation on image to close gaps between lines
% the kernel is a flat rectangular so that it helps to connect horizontal
% gaps
se=strel('rectangle',[10,30]);
bw=imdilate(bw,se);
% thin the lines to be single pixel line
bw=bwmorph(bw,'thin',inf);
% connect H bridge
bw=bwmorph(bw,'bridge');
imagesc(bw);colormap gray;axis image;
%% smooth the image, find the decreasing region, and apply the mask
imtmp = imgaussfilt(im0,3);
imtmp = diff(imtmp);
imtmp = [imtmp(1,:);imtmp];
intensity_decrease_mask = imtmp < 0;
bw = bw & intensity_decrease_mask;
imagesc(bw);colormap gray;axis image;
%%
% find properties of the lines, and find the longest lines
cc=regionprops(bw,'Area','PixelList','Centroid','MajorAxisLength','PixelIdxList');
% now select any lines that is larger than eighth of the image width
cc=cc([cc.MajorAxisLength]>size(bw,2)/8);
%%
% select lines that has average intensity larger than gray level
for i=1:length(cc)
cc(i).meanIntensity = mean(im0(sub2ind(size(im0),cc(i).PixelList(:,2), ...
cc(i).PixelList(:,1) )));
end
cc=cc([cc.meanIntensity]>150);
cnts=reshape([cc.Centroid],2,length(cc))';
%%
% calculate the minimum distance to the bottom right of each edge
for i=1:length(cc)
cc(i).distance2bottomright = sqrt(min((cc(i).PixelList(:,2)-size(im,1)).^2 ...
+ (cc(i).PixelList(:,1)-size(im,2)).^2));
end
% select the bottom edge
[~,minindex]=min([cc.distance2bottomright]);
bottomedge = cc(minindex);
%% clean up the lines a little bit
bwtmp = false(size(bw));
bwtmp(bottomedge.PixelIdxList)=1;
% find the end points to the most left and right
endpoints = bwmorph(bwtmp, 'endpoints');
[endy,endx] = find(endpoints);
[~,minind]=min(endx);
[~,maxind]=max(endx);
pos_most_left = [endx(minind),endy(minind)];
pos_most_right = [endx(maxind),endy(maxind)];
% select the shortest path between left and right
dists = bwdistgeodesic(bwtmp,pos_most_left(1),pos_most_left(2)) + ...
bwdistgeodesic(bwtmp,pos_most_right(1),pos_most_right(2));
dists(isnan(dists))=inf;
bwtmp = imregionalmin(dists);
bottomedge=regionprops(bwtmp,'PixelList');
%% plot the lines
imagesc(im0);colormap gray;axis image;hold on;axis off;
for i=1:length(cc)
plot(cc(i).PixelList(:,1),cc(i).PixelList(:,2),'b','linewidth',2);hold on;
end
plot(bottomedge.PixelList(:,1),bottomedge.PixelList(:,2),'r','linewidth',2);hold on;
print(gcf,num2str(ifile),'-djpeg');
% pause
end
关于image - MATLAB:通过曲折度/弧长进行层检测、矢量组合和选择，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/36360749/