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43 lines
1.7 KiB
Matlab
43 lines
1.7 KiB
Matlab
function [ normalisation_options ] = Parse_settings( sigma, patch_type, ratio_neg, num_samples, varargin)
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%PARSE_SETTINGS Summary of this function goes here
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% Detailed explanation goes here
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% creating the parameters to use when training colour (intensity) patches
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normalisation_options = struct;
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% this is what currently is expected (although could potentially have
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% bigger or smaller support regions
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normalisation_options.patchSize = [11 11];
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% The region size of a region that is taken for training around an
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% aligned or misaligned landmark
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if(sum(strcmp(varargin,'normalisation_size')))
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ind = find(strcmp(varargin,'normalisation_size')) + 1;
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normalisation_options.normalisationRegion = [varargin{ind}, varargin{ind}];
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else
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normalisation_options.normalisationRegion = [21 21];
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end
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% This specifies the split of data ratios
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normalisation_options.svmRatio = 0.8; % proportion of data used for training SVR
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normalisation_options.logitRatio = 0.1; % proportion of data for training logistic regressors
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% the rest is used for testing and provides the correlation and rms scores
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% should normalised cross correlation or just cross correlation should
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% be used on the patch as an SVR
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normalisation_options.useNormalisedCrossCorr = 1;
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% the patch types to be used (for now 'reg' (raw pixel values), and
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% 'grad' (gradient intensity values)
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normalisation_options.patch_type = patch_type;
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% number of training samples to use
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normalisation_options.numSamples = num_samples;
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normalisation_options.sigma = sigma;
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normalisation_options.rate_negative = ratio_neg;
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end
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