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Paper data
Unsupervised Segmentation of Textured Satellite and Aerial Images with Bayesian Methods

Wilson Simon, Trinity College Dublin
Zerubia Josiane, INRIA Sophia-Antipolis

Page numbers in the proceedings:
Volume III pp 477-480

Segmentation and Vision

Paper abstract
We investigate Bayesian solutions to unsupervised image segmentation based on the double Markov random field model. Inference on the number of classes in the image is done with reversible jump Metropolis moves. These moves are implemented by splitting and merging classes. Tests are conducted on satellite and aerial images.

A PDF version is available here