3 June 2011 Continuous quantification of uniqueness and stereoscopic vision
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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.
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Val Petran, Val Petran, Frank Merat, Frank Merat, "Continuous quantification of uniqueness and stereoscopic vision", Proc. SPIE 8056, Visual Information Processing XX, 80560E (3 June 2011); doi: 10.1117/12.883620; https://doi.org/10.1117/12.883620
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