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Proceedings Paper

Mine boundary detection using partially ordered Markov models
Author(s): Xia Hua; Jennifer L. Davidson; Noel A. C. Cressie
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Paper Abstract

Detection of objects in images in an automated fashion is necessary for many applications, including automated target recognition. In this paper, we present results of an automated boundary detection procedure using a new subclass of Markov random fields (MRFs), called partially ordered Markov models (POMMs). POMMs offer computational advantages over general MRFs. We show how a POMM can model the boundaries in an image. Our algorithm for boundary detection uses a Bayesian approach to build a posterior boundary model that locates edges of objects having a closed-loop boundary. We apply our method to images of mines with very good results.

Paper Details

Date Published: 14 October 1997
PDF: 12 pages
Proc. SPIE 3167, Statistical and Stochastic Methods in Image Processing II, (14 October 1997); doi: 10.1117/12.279638
Show Author Affiliations
Xia Hua, Iowa State Univ. (United States)
Jennifer L. Davidson, Iowa State Univ. (United States)
Noel A. C. Cressie, Iowa State Univ. (United States)

Published in SPIE Proceedings Vol. 3167:
Statistical and Stochastic Methods in Image Processing II
Francoise J. Preteux; Jennifer L. Davidson; Edward R. Dougherty, Editor(s)

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