Bayesian Inference in Model-Based Machine Vision
Thomas Binford, Tod Levitt, Wallace Mann
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
PDF Link: /papers/87/p73-binford.pdf
AUTHOR = "Thomas Binford
and Tod Levitt and Wallace Mann",
TITLE = "Bayesian Inference in Model-Based Machine Vision",
BOOKTITLE = "Uncertainty in Artificial Intelligence 3 Annual Conference on Uncertainty in Artificial Intelligence (UAI-87)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1987",
PAGES = "73--95"