Object recognition using Markov spatial processes

Author

Baddeley, A.J. ; van Lieshout, M.N.M.

Author_Institution

Centre for Math. & Comput. Sci., Amsterdam, Netherlands

fYear

1992

fDate

30 Aug-3 Sep 1992

Firstpage

136

Lastpage

139

Abstract

The Bayesian approach to image processing based on Markov random fields is adapted to image analysis problems such as object recognition and edge detection. In this context the prior models are Markov point processes and random object patterns from stochastic geometry. The authors develop analogues of J. Besag´s algorithm (1986). The erosion operator of mathematical morphology turns out to be a maximum likelihood estimator for a simple noise model. The authors show that the Hough transform can be interpreted as a likelihood ratio test statistic

Keywords

Bayes methods; Markov processes; image recognition; Bayesian approach; Hough transform; Markov point processes; Markov random fields; Markov spatial processes; edge detection; erosion operator; image processing; likelihood ratio test statistic; mathematical morphology; maximum likelihood estimator; object recognition; random object patterns; stochastic geometry; Bayesian methods; Context modeling; Geometry; Image edge detection; Image processing; Markov random fields; Morphology; Object recognition; Solid modeling; Stochastic resonance;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 1992. Vol.II. Conference B: Pattern Recognition Methodology and Systems, Proceedings., 11th IAPR International Conference on

Conference_Location

The Hague

Print_ISBN

0-8186-2915-0

Type

conf

DOI

10.1109/ICPR.1992.201739

Filename

201739