aux_merge_filters.m

Standard model object recognition matlab code

function f = aux_merge_filters (f1, s1, f2, s2, tag)

% FUNCTION f = aux_merge_filters (f1, s1, f2, s2, tag)
%
% Two filters f1 and f2 (of scale s1 and s2) are combined into one,
% and given a new tag.  
%
% More detail: Since the filter structures
% are stored in a sparse format, we can not simply concatenate two
% different matrices.  Although we can "unpack" the filter and then
% concatenate, this will not usually be a good method, since 4-D
% filters can be huge (many, many features in principle, and that's
% why we save filters in sparse format).  Thus, we will take some
% care in resizing the sparse versions of the filters before
% concatenation.

s1_tag = ['s' num2str(s1)];
s2_tag = ['s' num2str(s2)];

% Get the fields from each filter.
f1_f1    = getfield(f1, ['f1_' s1_tag]);
f1_f2    = getfield(f1, ['f2_' s1_tag]);
f1_i1    = getfield(f1, ['i1_' s1_tag]);
f1_i2    = getfield(f1, ['i2_' s1_tag]);
f1_i3    = getfield(f1, ['i3_' s1_tag]);
f1_size  = getfield(f1, ['size_' s1_tag]);
f1_shift = getfield(f1, ['shift_' s1_tag]);

f2_f1    = getfield(f2, ['f1_' s2_tag]);
f2_f2    = getfield(f2, ['f2_' s2_tag]);
f2_i1    = getfield(f2, ['i1_' s2_tag]);
f2_i2    = getfield(f2, ['i2_' s2_tag]);
f2_i3    = getfield(f2, ['i3_' s2_tag]);
f2_size  = getfield(f2, ['size_' s2_tag]);
f2_shift = getfield(f2, ['shift_' s2_tag]);

% We take the smaller shift size (take the denser sampling).
f_shift = min([min(f1_shift) min(f2_shift)]);

% f1_size(3) should be equal to f2_size(3), because we assume we
% are merging the filters from the same layer (ie. same number of
% afferents from the previous layer).  In the spatial dimension
% (1:2), we take the maximum size.

f_size      = zeros(1,4);
f_size(1:2) = max([f1_size(1:2); f2_size(1:2)]);
f_size(3)   = f1_size(3);
f_size(4)   = f1_size(4) + f2_size(4); % number of new features

f_f1 = put_into_same_size_and_merge (f1_f1, f2_f1, 0);
f_f2 = put_into_same_size_and_merge (f1_f2, f2_f2, 0);
% Note (0,1,1) -> 0 in sub2ind, so pad_val will be (0,1,1).
f_i1 = put_into_same_size_and_merge (f1_i1, f2_i1, 0);
f_i2 = put_into_same_size_and_merge (f1_i2, f2_i2, 1);
f_i3 = put_into_same_size_and_merge (f1_i3, f2_i3, 1);

f = [];
f = setfield(f, ['f1_' tag], f_f1);
f = setfield(f, ['f2_' tag], f_f2);
f = setfield(f, ['i1_' tag], f_i1);
f = setfield(f, ['i2_' tag], f_i2);
f = setfield(f, ['i3_' tag], f_i3);
f = setfield(f, ['size_' tag], f_size);
f = setfield(f, ['shift_' tag], f_shift);

function y = put_into_same_size_and_merge (x1, x2, pad_val)

% FUNCTION y = put_into_same_size_and_merge (x1, x2, pad_val)
%
% x1 and x2 are both 2-d matrices with different size.  We take the
% smaller one and put it into the larger size, and pad with pad_val.
% Then, we merge along the second dimension.

% Resize the smaller matrix.
if size(x1,1) > size(x2,1) 
  x2_tmp = ones(size(x1,1), size(x2,2))*pad_val;
  x2_tmp(1:size(x2,1),:) = x2;
  x2 = x2_tmp;
else
  x1_tmp = ones(size(x2,1), size(x1,2))*pad_val;
  x1_tmp(1:size(x1,1),:) = x1;
  x1 = x1_tmp;
end

% Merge along the second dimension.
y = cat(2, x1, x2);