init_filter_def.m~

Standard model object recognition matlab code

function [a,b,c,d] = init_filter_def (layer)

% FUNCTION [a,b,c,d] = init_filter_def (layer)
% 
% Output:
%   a = number of scales
%   b = pooling ranges in x and y, and num old/new features
%       Note b(4,:) is the number of total features, and hence,
%       it is different from the number of afferents.
%   c = shift
%   d = scale bands
%
% Some parameters are different for S- and C-layer units.  One
% should make sure that the progression of parameters makes sense.
% For example, going from C2b to VTU, the number of scales should
% go from 8 to 1.  The number of features from S2b to C2b should be
% the same, etc., etc.
% 
% Note that some parameter values shown below are "recorded" here
% for a reference (ie. the value here does not make any difference
% in the program), but by putting down all the parameters in one
% place should make debugging easier.  For example, S1 parameters
% are defined in filt_get_S1.m.

global learn_file;
default_num = 1000;

switch layer
  case 1 % S1
    % Defined also in filt_get_S1.m in more detail.
    a = 8;
    b = [9 9 1 8; 13 13 1 8; 17 17 1 8; 21 21 1 8; ...
	  25 25 1 8; 29 29 1 8; 33 33 1 8; 39 39 1 12]';
    c = repmat([1], [a 1]);
    d = [1 2 3 4 5 6 7 8];
  
  case 2 % C1
    a = 8;
    b = [8 8 8 4; 10 10 8 4; 12 12 8 4; 14 14 8 4; ...
	  16 16 8 4; 18 18 8 4; 20 20 8 4; 22 22 12 4]';
    c = [3 4 5 7 8 9 10 12];
    d = [1 2 3 4 5 6 7 8];
  
  case 3 % S2b
    a = 8;
    b = repmat([9 9 4 default_num], [a 1])';
    %b = repmat([3 3 4 default_num], [a 1])';
    c = repmat([3], [a 1]);
    c = repmat([1], [a 1]);
    d = [1 2 3 4 5 6 7 8];
  
  case 4 % C2b
    a = 8;
    b = repmat([40 40 default_num default_num], [a 1])';
    c = repmat([1], [a 1]); % Does not matter for C2b
    d = [1 1 1 1 1 1 1 1];
  
  case 5 % S2
    a = 8;
    b = repmat([3 3 4 default_num], [a 1])';
    c = repmat([2], [a 1]);
    d = [1:1:8];
  
  case 6 % C2
    a = 8;
    b = repmat([3 3 default_num default_num], [a 1])';
    c = repmat([2], [a 1]);
    d = [1 1 2 2 3 3 4 4];

  case 7 % S3
    % Especially for S3, it is important that the number of
    % features in b(3,:) matches that in S2, because the index i3
    % depends on the number of features from the previous layer.
    a = 4;
    b = repmat([3 3 default_num default_num], [a 1])';
    c = repmat([1], [a 1]);
    d = [1 2 3 4];
  
  case 8 % C3
    a = 4;
    b = repmat([40 40 default_num default_num], [a 1])';
    c = repmat([1], [a 1]);
    d = [1 1 1 1];
  
  case 9 % S4
    a = 1;
    b = repmat([1 1 default_num 100], [a 1]');
    c = repmat([1], [a 1]);
    d = [1];
      
  otherwise
    error('Layer not defined.');
end