Statistical models for multidisciplinary applications of image segmentation and labelling
Jan P. Cornelis, Edgard Nyssen, Antonis Katartzis, Luc M. van Kempen, Piet Boekaerts, Rudi Deklerck, ALEXANDRU SALOMIE · 2002
Three classes of statistical techniques used to solve image segmentation and labelling problems are reviewed: (1) supervised and unsupervised pixel classification, (2) exploitation of the probability distribution map as a way to model image structure, (3) Markov random field modelling combined with MAP statistical classification. Diverse examples illustrate the potential of the three approaches that are described as generic methods belonging to a common framework for image segmentation/labelling.