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Presentation
Mine boundary detection using partially ordered Markov models
Faculty of Informatics - Papers (Archive)
  • Xia Hua, Iowa State University
  • Jennifer Davidson, Iowa State University
  • Noel A. Cressie, Iowa State University
RIS ID
72974
Publication Date
1-1-1997
Publication Details

Hua, X., Davidson, J. & Cressie, N. A. (1997). Mine boundary detection using partially ordered Markov models. Proceedings of SPIE - The International Society for Optical Engineering, 3167, Statistical and Stochastic Methods in Image Processing II, (pp. 152-163).

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. 2004 Copyright SPIE - The International Society for Optical Engineering.

Citation Information
Xia Hua, Jennifer Davidson and Noel A. Cressie. "Mine boundary detection using partially ordered Markov models" (1997) p. 152 - 163
Available at: http://works.bepress.com/noel_cressie/141/