PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Image segmentation by polygonal Markov fields
R. Kluszczynski, Marie-Colette van Lieshout and T. Schreiber
Annals of the Institute of Statistical Mathematics 2005.

Abstract

This paper advocates the use of multi-coloured polygonal Markov fields for model-based image segmentation. The formal construction of consistent multi-coloured polygonal Markov fields by Arak--Clifford--Surgailis and its dynamic representation are specialised and adapted to our context. We then formulate image segmentation as a statistical estimation problem for a Gibbsian modification of an underlying polygonal Markov field, and discuss the choice of Hamiltonian. Monte Carlo techniques for estimating the model parameters and for finding the optimal partition of the image are developed. The approach is illustrated by means of toy examples.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:2399
Deposited By:Marie-Colette van Lieshout
Deposited On:22 November 2006