Knowledge-based interpretation of outdoor natural color scenes
Knowledge-based interpretation of outdoor natural color scenes
Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Evidential reasoning using stochastic simulation of causal models
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
A Markov Random Field Model-Based Approach to Image Interpretation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Causal networks: semantics and expressiveness
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Ascender II, a Visual Framework for 3D Reconstruction
ICVS '99 Proceedings of the First International Conference on Computer Vision Systems
Decision Making and Uncertainty Management in a 3D Reconstruction System
IEEE Transactions on Pattern Analysis and Machine Intelligence
Joint Segmentation and Image Interpretation Using Hidden Markov Models
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Segmentation and description of natural outdoor scenes
Image and Vision Computing
Information fusion for wireless sensor networks: Methods, models, and classifications
ACM Computing Surveys (CSUR)
An algorithm for cooperative learning of bayesian network structure from data
CSCWD'04 Proceedings of the 8th international conference on Computer Supported Cooperative Work in Design I
Optimization of image processing techniques using neural networks: a review
WSEAS Transactions on Information Science and Applications
Inferencing the graphs of causal Markov fields
Mathematical and Computer Modelling: An International Journal
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The problem of image interpretation is one of inference with the help of domain knowledge. In this correspondence, we formulate the problem as the maximum a posteriori (MAP) estimate of a properly defined probability distribution function. We show that a Bayesian network can be used to represent this p.d.f. as well as the domain knowledge needed for interpretation. The Bayesian network may be relaxed to obtain the set of optimum interpretations.