Over the last several years, a new representation for geometry has been developed, based on a 3-d probability distribution of surface position and appearance. This representation can be constructed from multiple images, using both still and video data. The probability for 3-d surface position is estimated in an on-line algorithm using Bayesian inference. The probability of a point belonging to a surface is updated as to its success in accounting for the intensity of the current image at the projected image location of the point. A Gaussian mixture is used to model image appearance. This update process can be proved to converge under relatively general conditions that are consistent with aerial imagery. There are no explicit surfaces extracted, but only discrete surface probabilities. This paper describes the application of this representation to object recognition, based on Bayesian compositional hierarchies.

Date Published: 4 May 2009
PDF: 12 pages
Proc. SPIE 7335, Automatic Target Recognition XIX, 73350Q (4 May 2009); doi: 10.1117/12.820753

Published in SPIE Proceedings Vol. 7335:
Automatic Target Recognition XIX
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)