Title :
Image interpretation using Bayesian networks
Author :
Kumar, V.P. ; Desai, U.B.
Author_Institution :
Dept. of Electr. & Comput. Eng., Northeastern Univ., Boston, MA, USA
fDate :
1/1/1996 12:00:00 AM
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;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
