SetTemplateParameters

 SetTemplateParameters - Set parameters for the image template.
 
   Parameters are hard coded in this .m file. 
   Please directly edit the code.


   Gabor feature parameters:

         Scales and the corresponding filter sizes
         0.2: 5 * 5
         0.3: 9 * 9
         0.4: 11 * 11
         0.6: 15 * 15
         0.8: 21 * 21
         1.2: 29 * 29
         1.6: 39 * 39

0001 function SetTemplateParameters
0002 % SetTemplateParameters - Set parameters for the image template.
0003 %
0004 %   Parameters are hard coded in this .m file.
0005 %   Please directly edit the code.
0006 %
0007 %
0008 %   Gabor feature parameters:
0009 %
0010 %         Scales and the corresponding filter sizes
0011 %         0.2: 5 * 5
0012 %         0.3: 9 * 9
0013 %         0.4: 11 * 11
0014 %         0.6: 15 * 15
0015 %         0.8: 21 * 21
0016 %         1.2: 29 * 29
0017 %         1.6: 39 * 39
0018 
0019 addpath('common');
0020 
0021 %% Feature parameters
0022 
0023 %
0024 % Use single precision numbers to save memory.
0025 %
0026 SinglePrecision = 1;
0027 
0028 %
0029 % Gabor scale, #orientations
0030 %
0031 GaborScale = single([0.2, 0.3, 0.4, 0.6, 0.8, 1.2, 1.6]);
0032 nGaborScale = length(GaborScale);
0033 nGaborOri = 16;
0034 GaborFilter = cell(nGaborScale*nGaborOri,1);
0035 GaborSymbol = cell(nGaborScale*nGaborOri,1);
0036 for iScale = 1:nGaborScale
0037     [ GaborFilter( (iScale-1)*nGaborOri + (1:nGaborOri) ) ...
0038         GaborSymbol( (iScale-1)*nGaborOri + (1:nGaborOri) ) ]...
0039         = makefilter(GaborScale(iScale), nGaborOri, SinglePrecision);
0040 end
0041 
0042 %
0043 % Correlation betweeen Gabor filters. Used for local inhibition.
0044 %
0045 CorrGabor = cell(nGaborOri*nGaborScale, nGaborOri*nGaborScale);
0046 GaborOverlapTolerance = 0.1;
0047 for iF = 1:nGaborOri*nGaborScale
0048     hi = (size(GaborFilter{iF}, 1) - 1) / 2;            % half size of filter iF
0049     for jF = 1:nGaborOri*nGaborScale
0050         hj = (size(GaborFilter{jF}, 1) - 1) / 2;        % half size of filter jF
0051         I = zeros(2*(hi+hj)+1)+sqrt(-1)*zeros(2*(hi+hj)+1);
0052         I((hj+1):(hj+2*hi+1), (hj+1):(hj+2*hi+1)) = GaborFilter{iF}; % put GaborFilter{iF} at the center
0053         % convolve GaborFilter{iF} by GaborFilter{jF}, 4 real-imaginary combinations
0054         rr = filter2(real(GaborFilter{jF}), real(I), 'same');
0055         ri = filter2(real(GaborFilter{jF}), imag(I), 'same');
0056         ir = filter2(imag(GaborFilter{jF}), real(I), 'same');
0057         ii = filter2(imag(GaborFilter{jF}), imag(I), 'same');
0058         if SinglePrecision > 0
0059             CorrGabor{iF, jF} = single( ((rr.*rr + ri.*ri + ir.*ir + ii.*ii)<GaborOverlapTolerance)*1.0 );
0060         else
0061             CorrGabor{iF, jF} = double( ((rr.*rr + ri.*ri + ir.*ir + ii.*ii)<GaborOverlapTolerance)*1.0 );
0062         end
0063         % CorrGabor{i,j} is how filter i at (0,0) inhibites filter j at locations overlapping with filter i
0064    end
0065 end
0066 
0067 
0068 %
0069 % Parameter for Gabor filter responses
0070 %
0071 GaborSaturation = 6;
0072 
0073 %
0074 % Parameter for exponential model
0075 %
0076 binSize = .2;  % binsize for computing histogram of q()
0077 numStoredPoint = 40; % number of stored lambda values
0078 spacing = .05; % spacing between two adjacent points
0079 
0080 % save
0081 save('template_params.mat');
0082 
0083