comp_crop_or_zeropad.m

Standard model object recognition matlab code

function x_new = comp_crop_or_zeropad (x, fsize, shift, type)

% FUNCTION x_new = comp_crop_or_zeropad (x, fsize, shift, type)
% 
% Depending on the operation type, x will be either cropped or
% padded with zeros to ensure proper convolution.
% For maximum (or soft-max) operations, zero-padding will be fine.
% For weighted sum, zero-padding will also be fine.
% For convolutions with oriented filters (such as S1 layer),
% zero-padding may introduce some artifacts if the background is
% not zeros; hence, cropping will be done.

% Set border_case option.
if strcmp(type,'gaussf') | strcmp(type,'normsp') | strcmp(type,'normdp') 
  border_care = 'crop';
else
  border_care = 'zero-pad';
end

if strcmp (border_care, 'crop')
  % check for the case when input image is smaller than the filter,
  % and "crop" was the border care option.
  if (size(x,1)<fsize(1)) | (size(x,2)<fsize(2))
    x_new = [];
  else
    for i = 1:2 % loop thru x and y direction
      % number of possible shifts that fits within the input.
      num_possible_shift = floor((size(x,i)-fsize(i))/shift);
      num_possible_shift = max([0 num_possible_shift]);
      remainder = size(x,i)-fsize(i)-shift*num_possible_shift;
      size_range(i) = size(x,i)-remainder;
    end
    x_new = zeros(size_range(1),size_range(2),size(x,3));
    x_new = x(1:size_range(1),1:size_range(2),:);
  end
elseif strcmp (border_care, 'zero-pad')
  for i = 1:2 % loop thru x and y direction
    num_possible_shift = floor((size(x,i)-fsize(i))/shift);
    num_possible_shift = max([0 num_possible_shift]);
    remainder = size(x,i)-fsize(i)-shift*num_possible_shift;
    if remainder > 0
      size_range(i) = size(x,i)+shift-remainder;
    elseif remainder == 0
      size_range(i) = size(x,i);
    elseif remainder < 0 % filter is larger than x.
      size_range(i) = fsize(i);
    end
  end
  x_new = zeros(size_range(1), size_range(2), size(x,3));
  x_new(1:size(x,1),1:size(x,2),:) = x;
end