Basic PCA based log-Likelihood Classifier: get_feature_vectors.m - File Exchange

Code covered by the BSD License

Highlights from
Basic PCA based log-Likelihood Classifier

[Lmean,Gmean,Cgppca,invCgppca... This function Train_Model makes the PCA matrices and mean values
[distanceL,distanceG,LogLikel... This function Apply_Model uses the matrices and means from train_model.m
example_classifier_ms.m
get_feature_vectors.m
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from Basic PCA based log-Likelihood Classifier by Dirk-Jan Kroon
PCA algorithm suitable for detection / recognition of 2D image "objects"

get_feature_vectors.m

% This MEX function will make feature vectors from local intensity regions
% in an image. Compile with "mex get_feature_vectors.c".
%
% F = get_feature_vectors(Iin,x,y,Rsizes)
% Inputs,
%      Iin : Input image gray scale or RGB (or more colors)
%      x,y : Coordinate in the image
%      Rsizes : The size of the local intensity patch [sizex, sizey]
%
% Outputs,
%      A matrix with the intensity feature vectors, first feature
%          vector is F(:,1);
%
% Function is written by D.Kroon University of Twente (July 2009)

Highlights from Basic PCA based log-Likelihood Classifier

Highlights from
Basic PCA based log-Likelihood Classifier