Proposals:Refactoring Statistics Framework 2007 Background

The main components of a classification framework are

1. Input
   1. Image
   2. Data points
2. Membership models
   1. Can be manually set or automatically generated from the sample data
   2. Estimators are available to generate membership functions ( ImageModelEstimatorBase, ImageGuassianModelEstimator,ExpectationMaximizationMixtureModelEstimator )
   3. Some classes are named with Estimator suffix but they do more than just estimating membership functions
      1. itkKdTreeBasedKmeansEstimator
3. Distance functions
4. Decison Rules
5. Classifiers

Note:

1. ITK contains classes which combine all these components into one framework such as itkScalarImageKmeansImageFilter and itkBayesianClassifierImageFilter