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Proceedings Paper

Probabilistic Foundations For Information Fusion With Applications To Combining Stereo And Contour
Author(s): David Shulman; John (Yiannis) Aloimonos
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Paper Abstract

Many general frameworks exist for fusion of information from several sources. Among them are random fields, Dempster-Shafer, fuzzy sets. They all can be considered as computationally convenient approximations to a true probabilistic analysis of the errors in constraints relating data and unknowns. In fact all problems of combination of evidence can be given a common formulation in terms of regularization theory. Such a theory can even be extended to allow for discontinuities in the unknowns. At the most abstract level, the information fusion process is simply reconciling a priori constraints on the unknowns (constraints of smoothness that really do not depend on the particular cues being used with different data constraints. So it is crucial to find convenient, reliable constraints, ideally one data constraint relating several data cues. We show how this is possible for the case of stereo and planar contour and some of the problems involved in extending to the non-planar case. The non-planar case is difficult but at least there is a way (provided by contour) to lessen the amount of search stereo demands.

Paper Details

Date Published: 1 March 1990
PDF: 5 pages
Proc. SPIE 1198, Sensor Fusion II: Human and Machine Strategies, (1 March 1990); doi: 10.1117/12.969989
Show Author Affiliations
David Shulman, University of Maryland (United States)
John (Yiannis) Aloimonos, University of Maryland (United States)

Published in SPIE Proceedings Vol. 1198:
Sensor Fusion II: Human and Machine Strategies
Paul S. Schenker, Editor(s)

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