Contagion-driven image segmentation and labeling

A. Banerjee, P. Burlina, F. Alajaji

Research output: Contribution to conferencePaperpeer-review

Abstract

We propose a segmentation method based on Polya's urn model for contagious phenomena. An 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.

Original languageEnglish (US)
Pages255-260
Number of pages6
StatePublished - Dec 1 1998
EventProceedings of the 1998 IEEE 6th International Conference on Computer Vision - Bombay, India
Duration: Jan 4 1998Jan 7 1998

Other

OtherProceedings of the 1998 IEEE 6th International Conference on Computer Vision
CityBombay, India
Period1/4/981/7/98

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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