Image correspondences using cross-correlation
16 Apr 2010
09 Dec 2011)
Find matching features in pairs of images using normalised cross-correlation: class file and demo.
|patch_var(x, psize, shape)
function y = patch_var(x, psize, shape)
%PATCH_VAR Sliding variance
% Y = PATCH_VAR(X, PSIZE) returns a matrix Y each element of which is
% the variance of a patch of X. X must be 2-D and contain at least one
% patch. PSIZE gives the size of the patch: if it is a scalar than the
% patch is PSIZE-by-PSIZE; otherwise PSIZE should be a 2-element
% vector giving the numbers of rows and columns in the patch. Y will
% be smaller than X as zero padding is not done: if size(X) is [nr,nc]
% then size(Y) will be [nr-PSIZE(1)+1, nc-PSIZE(2)+1].
% Y = PATCH_VAR(X, PSIZE, SHAPE) is the same except that SHAPE specifies
% the boundary behaviour as for CONVOLVE2. For example, 'reflect' may be
% used to cause Y to be the same size as X.
% Copyright David Young 2010
% This is very much more efficient (both in time and memory) than
% using COLFILT with VAR.
if nargin < 3
shape = 'valid';
m = ones(psize); % averaging mask
n = numel(m);
if strcmp(shape, 'valid')
x = x - mean(x(:)); % improve stability if possible
a = convolve2(x, m, shape);
as = convolve2(x.*x, m, shape);
% Best to divide result not mask, as svd in convolve2 is
% sometimes slower if mask is not ones(!)
y = (as - (a.*a)/n )/n;