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

[Luis Ibanez]

Hi Kostas,


Please read the ITK Software Guide

    http://www.itk.org/ItkSoftwareGuide.pdf

in particular the Statistics Chapter.


--

You will find that the Statistics framework in ITK has
been designed from the base to manage multi-feature
statistics.

You simply need to represent your 5D data as
FixedArray<double,5> and use this type as template
argument of the itk::Sample class.


All of this will be clear once you read the chapter of
the ITK Software Guide.


Please let us know if you have any further questions,



    Thanks


      Luis



------------------------------
Kostas Rapantzikos wrote:

> 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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