I have written a 2D histogram algorithm for 2 matlab vectors. Unfortunately, I cannot figure out how to vectorize it, and it is about an order of magnitude too slow for my needs. Here is what I have:
function [ result ] = Hist2D( vec0, vec1 )
%Hist2D takes two vectors, and computes the two dimensional histogram
% of those images. It assumes vectors are non-negative, and bins
% are the integers.
%
% OUTPUTS
% result -
% size(result) = 1 + [max(vec0) max(vec1)]
% result(i,j) = number of pixels that have value
% i-1 in vec0 and value j-1 in vec1.
result = zeros(max(vec0)+1, max(vec1)+1);
fvec0 = floor(vec1)+1;
fvec1 = floor(vec0)+1;
% UGH, This is gross, there has to be a better way...
for i = 1 : size(fvec0);
result(fvec0(i), fvec1(i)) = 1 + result(fvec0(i), fvec1(i));
end
end
Thoughts?
Thanks!! John
Description. histogram2( X,Y ) creates a bivariate histogram plot of X and Y . The histogram2 function uses an automatic binning algorithm that returns bins with a uniform area, chosen to cover the range of elements in X and Y and reveal the underlying shape of the distribution.
In image processing a 2D histogram shows the relationship of intensities at the exact position between two images. The 2D histogram is mostly used to compare 2 channels in a multi-channel images, where the x-axis represent the intensities of the first channel and the y-axis the intensities of the second channel.
histogram displays the bins as rectangles such that the height of each rectangle indicates the number of elements in the bin. histogram( X , nbins ) uses a number of bins specified by the scalar, nbins . histogram( X , edges ) sorts X into bins with the bin edges specified by the vector, edges .
Here is my version for a 2D histogram:
%# some random data
X = randn(2500,1);
Y = randn(2500,1)*2;
%# bin centers (integers)
xbins = floor(min(X)):1:ceil(max(X));
ybins = floor(min(Y)):1:ceil(max(Y));
xNumBins = numel(xbins); yNumBins = numel(ybins);
%# map X/Y values to bin indices
Xi = round( interp1(xbins, 1:xNumBins, X, 'linear', 'extrap') );
Yi = round( interp1(ybins, 1:yNumBins, Y, 'linear', 'extrap') );
%# limit indices to the range [1,numBins]
Xi = max( min(Xi,xNumBins), 1);
Yi = max( min(Yi,yNumBins), 1);
%# count number of elements in each bin
H = accumarray([Yi(:) Xi(:)], 1, [yNumBins xNumBins]);
%# plot 2D histogram
imagesc(xbins, ybins, H), axis on %# axis image
colormap hot; colorbar
hold on, plot(X, Y, 'b.', 'MarkerSize',1), hold off
Note that I removed the "non-negative" restriction, but kept integer bin centers (this could be easily changed into dividing range into equally-sized specified number of bins instead "fractions").
This was mainly inspired by @SteveEddins blog post.
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