Statistical models for multidisciplinary applications of image segmentation and labelling

Author

Cornelis, J. ; Nyssen, E. ; Katartzis, A. ; van Kempen, L. ; Boekaerts, P. ; Deklerck, R. ; Salomie, A.

Author_Institution

Dept. of Electron. & Inf. Process., Vrije Univ., Brussels, Belgium

Volume

3

fYear

2000

fDate

2000

Firstpage

2103

Abstract

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

Keywords

Markov processes; image classification; image segmentation; maximum likelihood estimation; probability; random processes; statistical analysis; unsupervised learning; MAP statistical classification; Markov random field modelling; image segmentation; image structure; labelling; multidisciplinary applications; probability distribution map; statistical models; supervised pixel classification; unsupervised pixel classification; Biomedical imaging; Image analysis; Image processing; Image segmentation; Information processing; Labeling; Markov random fields; Pixel; Probability distribution; Shape measurement;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing Proceedings, 2000. WCCC-ICSP 2000. 5th International Conference on

Conference_Location

Beijing

Print_ISBN

0-7803-5747-7

Type

conf

DOI

10.1109/ICOSP.2000.893520

Filename

893520