[Insight-users] multi-feature classification...

[Kostas Rapantzikos]

Hallo to everyone,
 
i am new to ITK, so please forgive me for the simple question.
 
I want to implement a maximum likelihood classifier. The feature vector is
5-dimensional, which means that i have 5 values for a single pixel (image
classification). Although i have read the examples
(ExpectationMaximizationMixtureModelEstimator) with the two 1D Gaussians, i
haven't been able to feed the classifier in the correct way. What's the
right way to represent the input data? How are the measurement vectors
suppose to be filled? What i actually need is the correct way to change the
following lines (acquired from ITK example), in order to obtain a
classification on a 5x18432 (features X #pixels) input matrix.
 
  typedef itk::Vector< double, 1 > MeasurementVectorType;
  typedef itk::Statistics::ListSample< MeasurementVectorType > SampleType;
  SampleType::Pointer sample = SampleType::New();
 
Thanks in advance,
Kostas

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