Share Email Print
cover

Proceedings Paper

Mine boundary detection using Markov random field models
Author(s): Xia Hua; Jennifer L. Davidson; Noel A. C. Cressie
Format Member Price Non-Member Price
PDF $14.40 $18.00

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 boundary detection using Markov random fields. Once the boundaries of regions are detected, object recognition can be conducted to classify the regions within the boundaries. Thus, an approach that gives good boundary detection is very important in many automated target recognition systems. Our algorithm for boundary detection combines Bayesian approach with a histogram specification technique to locate edges of objects that have a closed-loop boundary. The boundary image is modeled by a Markov random field. The method is relatively insensitive to the input parameters required by the user and provides a fairly robust automated detection procedure that produces an image with closed one-pixel-wide boundaries. We apply our method to mine data with very good results.

Paper Details

Date Published: 20 June 1995
PDF: 11 pages
Proc. SPIE 2496, Detection Technologies for Mines and Minelike Targets, (20 June 1995); doi: 10.1117/12.211359
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. 2496:
Detection Technologies for Mines and Minelike Targets
Abinash C. Dubey; Ivan Cindrich; James M. Ralston; Kelly A. Rigano, Editor(s)

© SPIE. Terms of Use
Back to Top