Title :
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
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;
Conference_Titel :
Signal Processing Proceedings, 2000. WCCC-ICSP 2000. 5th International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-5747-7
DOI :
10.1109/ICOSP.2000.893520
