DocumentCode
2676195
Title
Unsupervised image segmentation based on the comparison of local and regional histograms
Author
Dingle, Alison A. ; Morrison, Mark W.
Author_Institution
Centre for Intelligent Inf. Process. Syst., Western Australia Univ., Nedlands, WA, Australia
Volume
3
fYear
1996
fDate
16-19 Sep 1996
Firstpage
959
Abstract
This paper proposes an new method for unsupervised segmentation of images which does not rely on parametric modelling of the observed images. Furthermore, the problem of finding the number of image classes is carried out as an integral part of the segmentation process, rather than by resorting to goodness-of-fit cluster validation measures, such as AIC or MDL. A brief overview of the algorithm is given, as well as examples of its application to both synthetic and real images
Keywords
Markov processes; biomedical NMR; image segmentation; nonparametric statistics; unsupervised learning; Markov random fields; image classes; local histograms; magnetic resonance medical images; real images; regional histograms; synthetic images; unsupervised image segmentation; Australia; Clustering algorithms; Histograms; Image segmentation; Information processing; Intelligent systems; Markov random fields; Parametric statistics; Pixel; Stochastic processes;
fLanguage
English
Publisher
ieee
Conference_Titel
Image Processing, 1996. Proceedings., International Conference on
Conference_Location
Lausanne
Print_ISBN
0-7803-3259-8
Type
conf
DOI
10.1109/ICIP.1996.560982
Filename
560982
Link To Document