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Proceedings Paper

Continuous quantification of uniqueness and stereoscopic vision
Author(s): Val Petran; Frank Merat
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Paper Abstract

In this paper we introduce the concept of continuous quantification of uniqueness. Our approach is to construct an algorithm that computes a fuzzy set membership function, which given any inter-object dissimilarity metric and it's variability, measures the probability that an entity of interest will not be confused with other similar entities in a search space. We demonstrate use of this algorithm by applying it to stereoscopic computer vision, in order to identify which of several sub-problems pertaining to solution of the classic stereoscopic correspondence problem are least likely to be solved incorrectly, and hence are most well suited to greatest confidence first approaches.

Paper Details

Date Published: 3 June 2011
PDF: 15 pages
Proc. SPIE 8056, Visual Information Processing XX, 80560E (3 June 2011); doi: 10.1117/12.883620
Show Author Affiliations
Val Petran, Artificial Perception Technologies Inc. (United States)
Frank Merat, Case Western Reserve Univ. (United States)


Published in SPIE Proceedings Vol. 8056:
Visual Information Processing XX
Zia-ur Rahman; Stephen E. Reichenbach; Mark Allen Neifeld, Editor(s)

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