如何避免在 matlab 中嵌套 for 循环?
How to avoid nested for loops in matlab?
我正在根据图像中像素的强度差异构建邻接表。
Matlab中的代码片段如下:
m=1;
len = size(cur_label, 1);
for j=1:len
for k=1:len
if(k~=j) % avoiding diagonal elements
intensity_diff = abs(indx_intensity(j)-indx_intensity(k)); %intensity defference of two pixels.
if intensity_diff<=10 % difference thresholded by 10
adj_list(m, 1) = j; % storing the vertices of the edge
adj_list(m, 2) = k;
m = m+1;
end
end
end
end
y = sparse(adj_list(:,1),adj_list(:,2),1); % creating a sparse matrix from the adjacency list
如何避免这些讨厌的嵌套 for 循环?如果图像尺寸很大,那么它的工作就像灾难一样。如果有人有任何解决方案,那对我来说将是一个很大的帮助。
问候
拉特那
我在这里假设输入 indx_intensity
作为一个 1D
数组。有了这个假设,这里有一个 broadcasting/bsxfun
-
的矢量化方法
%// Threshold parameter
thresh = 10;
%// Get elementwise differentiation between elements in indx_intensity
diffs = abs(bsxfun(@minus,indx_intensity(:),indx_intensity(:).')) %//'
%// Threshold the differentiations against the threshold, thus giving us a
%// 2D square matrix. Then, set the diagonal elements to zero to avoid them.
mask = diffs <= thresh;
mask(1:len+1:end) = 0;
%// Get the indices of the TRUE elements in the valid mask as final output.
[R,C] = find(mask);
adj_list_out = [C R];
我正在根据图像中像素的强度差异构建邻接表。 Matlab中的代码片段如下:
m=1;
len = size(cur_label, 1);
for j=1:len
for k=1:len
if(k~=j) % avoiding diagonal elements
intensity_diff = abs(indx_intensity(j)-indx_intensity(k)); %intensity defference of two pixels.
if intensity_diff<=10 % difference thresholded by 10
adj_list(m, 1) = j; % storing the vertices of the edge
adj_list(m, 2) = k;
m = m+1;
end
end
end
end
y = sparse(adj_list(:,1),adj_list(:,2),1); % creating a sparse matrix from the adjacency list
如何避免这些讨厌的嵌套 for 循环?如果图像尺寸很大,那么它的工作就像灾难一样。如果有人有任何解决方案,那对我来说将是一个很大的帮助。 问候 拉特那
我在这里假设输入 indx_intensity
作为一个 1D
数组。有了这个假设,这里有一个 broadcasting/bsxfun
-
%// Threshold parameter
thresh = 10;
%// Get elementwise differentiation between elements in indx_intensity
diffs = abs(bsxfun(@minus,indx_intensity(:),indx_intensity(:).')) %//'
%// Threshold the differentiations against the threshold, thus giving us a
%// 2D square matrix. Then, set the diagonal elements to zero to avoid them.
mask = diffs <= thresh;
mask(1:len+1:end) = 0;
%// Get the indices of the TRUE elements in the valid mask as final output.
[R,C] = find(mask);
adj_list_out = [C R];