fast 2dimensional histograming in matlab

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

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.

You could do something like:

max0 = max(fvec0) + 1;
max1 = max(fvec1) + 1;

% Combine the vectors
combined = fvec0 + fvec1 * max0;

% Generate a 1D histogram
hist_1d = hist(combined, max0*max1);

% Convert back to a 2D histogram
hist_2d = reshape(hist, [max0 max1]);

(Note: untested)