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
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;
Conference_Titel :
Image Processing, 1996. Proceedings., International Conference on
Conference_Location :
Lausanne
Print_ISBN :
0-7803-3259-8
DOI :
10.1109/ICIP.1996.560982
