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.

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

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