Contagion-driven image segmentation and labeling

Author

Banerjee, A. ; Burlina, P. ; Alajaji, F.

Author_Institution

Center for Autom. Res., Maryland Univ., College Park, MD, USA

fYear

1998

fDate

4-7 Jan 1998

Firstpage

255

Lastpage

260

Abstract

We propose a segmentation method based on Polya´s urn model for contagious phenomena. Initial labeling of the pixel is obtained using a Maximum Likelihood (ML) estimate or the Nearest Mean Classifier (NMC), which are used to determine the initial composition of an urn representing the pixel. The resulting urns are then subjected to a modified urn sampling scheme mimicking the development of an infection to yield a segmentation of the image into homogeneous regions. Examples of the application of this scheme to the segmentation of synthetic texture images, Ultra-Wideband Synthetic Aperture Radar (UWB SAR) images and Magnetic Resonance Images (MRI) are provided

Keywords

image classification; image segmentation; image texture; maximum likelihood estimation; Maximum Likelihood estimate; Nearest Mean Classifier; Polya´s urn model; contagious phenomena; image segmentation; labeling; segmentation; synthetic texture images; Annealing; Context modeling; Image sampling; Image segmentation; Labeling; Lattices; Maximum likelihood estimation; Pixel; Stochastic processes; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Vision, 1998. Sixth International Conference on

Conference_Location

Bombay

Print_ISBN

81-7319-221-9

Type

conf

DOI

10.1109/ICCV.1998.710727

Filename

710727