LearnEllipsoidTemplate

 LearnEllipsoidTemplate - Learns an active basis template for Ellipsoid

0001 % LearnEllipsoidTemplate - Learns an active basis template for Ellipsoid
0002 
0003 clear all; close all; 
0004 disp('===============> Learning the active basis model from positive training images');
0005 
0006 load ../template_params
0007 
0008 %% generate the training image
0009 numImage = 1; % number of training images
0010 I = cell(numImage, 1);
0011 sizex = 63; sizey = 63;
0012 center = 32; a = 22; b = 10;
0013 I0 = single(zeros(sizex, sizey)); 
0014 for x = 1:sizex
0015         for y = 1:sizey
0016             if (((x-center)/a)^2+((y-center)/b)^2<1.)
0017                 I0(x, y) = single(1.); 
0018             end
0019         end
0020 end
0021 I{1} = I0+0.;
0022 
0023 allSizex = zeros(1, numImage, 'single')+sizex; 
0024 allSizey = zeros(1, numImage, 'single')+sizey;
0025 %% learning a template
0026 
0027 
0028 % use the 3rd scale
0029 s = 4;
0030 storeName = ['../result/naturalStat' num2str(s-1)];
0031 load(storeName);
0032 numElement = 14;
0033 halfFilterSize = ( size(GaborFilter{(s-1)*nGaborOri+1},1) - 1 ) / 2;
0034 disp(['start filtering training images at Gabor length ' num2str(2*halfFilterSize+1)]); tic
0035 
0036 selectedOrient = zeros(numElement, 1, 'single');  % orientation and location of selected Gabors
0037 selectedx = zeros(numElement, 1, 'single');
0038 selectedy = zeros(numElement, 1, 'single');
0039 selectedlambda = zeros(numElement, 1, 'single'); % weighting parameter for scoring template matching
0040 selectedLogZ = zeros(numElement, 1, 'single'); % normalizing constant
0041 symbolicTemplate = zeros(sizex, sizey, 'single'); % template of active basis
0042 
0043 
0044 %% Compute SUM1 maps by Gabor filtering
0045 SUM1map = ApplyfilterfftsameLocalNormPerOri(I, GaborFilter((s-1)*nGaborOri+(1:nGaborOri))); % SUM1 maps by Gabor filtering
0046 disp(['filtering time: ' num2str(toc) ' seconds']);
0047 
0048 %% Compute MAX1 maps and record the track maps
0049 locationShiftLimit = 0; % shift in normal direction = locationShiftLimit*subsample pixels
0050 orientShiftLimit = 0; % shift in orientation
0051 Csigmoid(numImage, allSizex, allSizey, nGaborOri, GaborSaturation, SUM1map);
0052 MAX1map = cell(numImage, nGaborOri);
0053 subsampleM1 = 1;  % to be safe, please refrain from setting it to be larger than 1
0054 trackMap = cell(numImage, nGaborOri);
0055 for img = 1:numImage
0056     for orient = 1:nGaborOri
0057         MAX1map{img, orient} = single(zeros(floor(sizex/subsampleM1), floor(sizey/subsampleM1))); 
0058         trackMap{img, orient} = single(zeros(floor(sizex/subsampleM1), floor(sizey/subsampleM1))); 
0059     end
0060 end
0061 disp(['start maxing training images at Gabor length ' num2str(2*halfFilterSize+1)]); tic
0062 CgetMAX1track(numImage, allSizex, allSizey, nGaborOri, locationShiftLimit, orientShiftLimit, subsampleM1, ...
0063               SUM1map, MAX1map, trackMap);
0064 disp(['maxing time: ' num2str(toc) ' seconds']);
0065 
0066 
0067 %% Learning by shared sketch algorithm
0068 learningSampleSize = 1;
0069 deformedTemplate = cell(1, learningSampleSize); % templates for training images
0070 for img = 1 : learningSampleSize
0071     deformedTemplate{img} = single(zeros(sizex, sizey));  
0072 end
0073 SUM2score = zeros(learningSampleSize, 1); % template matching scores for training images
0074 disp(['start mex-C learning of template at Gabor length ' num2str(2*halfFilterSize+1)]); tic
0075 CsharedSketch(nGaborOri, locationShiftLimit, orientShiftLimit, GaborSaturation, subsampleM1, ... % about active basis  
0076        numElement, learningSampleSize, sizex, sizey, ...
0077        SUM1map, MAX1map, trackMap, ... % about training images 
0078        halfFilterSize, CorrGabor((s-1)*nGaborOri + (1:nGaborOri), (s-1)*nGaborOri + (1:nGaborOri)),...
0079        GaborSymbol( (s-1)*nGaborOri + (1:nGaborOri) ), ... % about filters
0080        numStoredPoint, (storedLambda), (storedExpectation), (storedLogZ), ... % about exponential model 
0081        selectedOrient, selectedx, selectedy, selectedlambda, selectedLogZ, SUM2score, ... % learned parameters
0082        symbolicTemplate, deformedTemplate); % learned templates
0083 
0084 selectedScale = ones(numElement,1)*(s-1); % the scale index starts from 0
0085    
0086     %% Display and save results
0087 %         OutLearningResult;
0088 %         Outepsgif(-symbolicTemplate,
0089 %         ['output/learnedTemplateUsingGaborScale' num2str(s)]); % output
0090 %         learned template
0091 
0092 disp(['mex-C learning time: ' num2str(toc) ' seconds']);
0093 templateName = ['../result/learnedEllipsoidTemplate' num2str(s-1)];
0094 save(templateName, 'numElement', 'selectedOrient', 'selectedx', 'selectedy', 'selectedScale', 'selectedlambda', 'selectedLogZ', ...
0095                    'sizex', 'sizey', 'symbolicTemplate', 'subsampleM1');
0096