Uncertainty in Artificial Intelligence
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Bayesian Inference in Model-Based Machine Vision
Thomas Binford, Tod Levitt, Wallace Mann
Abstract:
This is a preliminary version of visual interpretation integrating multiple sensors in SUCCESSOR, an intelligent, model-based vision system. We pursue a thorough integration of hierarchical Bayesian inference with comprehensive physical representation of objects and their relations in a system for reasoning with geometry, surface materials and sensor models in machine vision. Bayesian inference provides a framework for accruing_ probabilities to rank order hypotheses.
Keywords: Multiple Sensors, SUCCESSOR, Model-Based
Pages: 86-97
PS Link:
PDF Link: /papers/87/p86-binford.pdf
BibTex:
@INPROCEEDINGS{Binford87,
AUTHOR = "Thomas Binford and Tod Levitt and Wallace Mann",
TITLE = "Bayesian Inference in Model-Based Machine Vision",
BOOKTITLE = "Proceedings of the Third Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-87)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "1987",
PAGES = "86--97"
}


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