3 June 2011 Continuous quantification of uniqueness and stereoscopic vision
Author Affiliations +
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.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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

Back to Top