A hierarchical evidence accumulation scheme developed for use in blackboard systems is described. This scheme, based on the Dempster-Shafer formalism, uses a computationally efficient variation of Dempster's rule of combination enabling the system to deal with the overwhelming amount of information present in image data. This variation of Dempster's rule allows the reasoning process to be embedded into the abstraction hierarchy by allowing for the propagation of belief values between elements at different levels of abstraction. The evidence accumulation scheme described here was originally designed to be embedded in PSEIKI, a blackboard system for expectation-driven interpretation of image data. PSEIKI performs expectation-driven processing by matching image-elements, such as edges and regions, with model-elements from a supplied expected scene. PSEIKI builds abstraction hierarchies in image data using cues taken from the supplied abstractions in the expected scene. Hypothesized abstractions in the image data are geometrically compared with the known abstractions in the expected scene; the metrics used for these comparisons translate into belief values. The evidence accumulation system is described in detail and a few representative metrics also are presented.

Date Published: 1 October 1991
PDF: 12 pages
Proc. SPIE 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, (1 October 1991); doi: 10.1117/12.48365

Published in SPIE Proceedings Vol. 1569:
Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision
Su-Shing Chen, Editor(s)