Statistical pattern recognition in image analysis

Many tasks in image analysis can be formulated as problems of discrimination or, generally, of pattern recognition. A pattern-recognition system is normally considered to comprise two processing stages: the feature selection and extraction stage, which attempts to reduce the dimensionality of the pattern to be classified, and the classification stage, the purpose of which is to assign the pattern into its perceptually meaningful category. This paper gives an overview of the various approaches to designing statistical pattern recognition schemes. The problem of feature selection and extraction is introduced. The discussion then focuses on statistical decision theoretic rules and their implementation. Both parametric and non-parametric classification methods are covered. The emphasis then switches to decision making in context. Two basic formulations of contextual pattern classification are put forward, and the various methods developed from these two formulations are reviewed. These include the method of hidden Markov chains, the Markov random field approach, Markov meshes, and probabilistic and discrete relaxation.