Sampling pixel intensities according to distance matrix...
조회 수: 2 (최근 30일)
이전 댓글 표시
I have Mat(X) that consists of distances; lets say
Mat(X) = [2 1 2; 1 0 1; 2 1 2]
And an image, let's call it Mat(Y), containing an intensity value in every i, j element; for example
Mat(Y) = [9 5 6; 7 1 3; 2 8 4]
I would like a vector describing the average pixel intensity at the distances described by Mat(X) such that
y(0) = 1
y(1) = (5+3+7+8)/4
y(2) = (9+6+4+2)/4
I am not a very saavy coder as I imagine that this should not be very difficult to do, yet I am struggling to make it happen; ANY POINTERS ARE GREATLY APPRECIATED ! ! !
댓글 수: 0
채택된 답변
Voss
2021년 10월 12일
Let X be your matrix of distances and Y be your matrix of intensities. Then the following code makes use of logical indexing to calculate the average value of Y at each unique value of X:
uX = unique(X(:)); % vector of unique distances
n_uX = numel(uX); % number of unique distances
uY = zeros(1,n_uX); % initialize average intensity vector
for i = 1:n_uX % for each unique distance
uY(i) = mean(Y(X == uX(i))); % average intensity is the mean of the intensities where distance == that unique distance
end
댓글 수: 2
DGM
2021년 10월 12일
Might also want to round X so that the equality test works reliably. If other binning methods are used, it might be good to test for equality with tolerance.
추가 답변 (2개)
Image Analyst
2021년 10월 12일
Use splitapply() which was meant for this kind of thing:
MatX = [2 1 2; 1 0 1; 2 1 2]
% And an image, let's call it Mat(Y), containing an intensity value in every i, j element; for example
MatY = [9 5 6; 7 1 3; 2 8 4]
theMeans = splitapply(@mean, MatY(:), MatX(:)+1)
theMeans =
1
5.75
5.25
댓글 수: 4
Image Analyst
2021년 10월 18일
So did my code work for you like it did for me? Are we done here? If not, attach your nonworking code and nonworking data.
DGM
2021년 10월 12일
편집: DGM
2021년 10월 12일
Disregarding splitapply() for a moment, the issue of working with non-integers can be avoided by using the histogram tools to bin the distance array as desired.
X = [2 1 2; 1 0 1; 2 1 2]/100;
Y = [9 5 6; 7 1 3; 2 8 4];
nbins = 3; % you probably want more than 3
[~,~,idx] = histcounts(X,nbins);
binmeans = zeros(nbins,1);
for b = 1:nbins
binmeans(b) = mean(Y(idx == b));
end
binmeans
If you want to use splitapply instead of the loop, you can do that too:
X = [2 1 2; 1 0 1; 2 1 2]/100;
Y = [9 5 6; 7 1 3; 2 8 4];
nbins = 3; % you probably want more than 3
[~,~,idx] = histcounts(X,nbins);
binmeans2 = splitapply(@mean,Y(:),idx(:))
I'm sure findgroups would work too.
X = [2 1 2; 1 0 1; 2 1 2]/100;
Y = [9 5 6; 7 1 3; 2 8 4];
nbins = 3; % you probably want more than 3
idx = findgroups(X(:));
binmeans2 = splitapply(@mean,Y(:),idx(:))
Findgroups may be simpler to use than assuming that groups are uniformly distributed (as with histogram tools). Depends on what you want, I guess.
댓글 수: 0
참고 항목
카테고리
Help Center 및 File Exchange에서 Read, Write, and Modify Image에 대해 자세히 알아보기
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!