Image interpretation using Bayesian networks

Author

Kumar, V.P. ; Desai, U.B.

Author_Institution

Dept. of Electr. & Comput. Eng., Northeastern Univ., Boston, MA, USA

Volume

18

Issue

1

fYear

1996

fDate

1/1/1996 12:00:00 AM

Firstpage

74

Lastpage

77

Abstract

The problem of image interpretation is one of inference with the help of domain knowledge. In this paper, we formulate the problem as the maximum a posteriori (MAP) estimate of a properly defined probability distribution function (PDF). We show that a Bayesian network can be used to represent this PDF as well as the domain knowledge needed for interpretation. The Bayesian network may be relaxed to obtain the set of optimum interpretations

Keywords

Bayes methods; image recognition; inference mechanisms; knowledge based systems; object recognition; probability; Bayesian networks; Markov random field; artificial intelligence; decision making; domain knowledge; expert systems; image interpretation; inference; maximum a posteriori estimate; object recognition; probability distribution function; Bayesian methods; Decision making; Expert systems; Image segmentation; Intelligent networks; Labeling; Markov random fields; Object recognition; Pixel; Probability distribution;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/34.476423

Filename

476423