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Proceedings Paper

A comparison of some predictors of stereoscopic match correctness
Author(s): Val Petran; Frank Merat
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Paper Abstract

Previously we introduced the concept of continuous quantification of uniqueness, as a general purpose technique designed to be applicable to any situation in which there is a need to decide which of several equally effective objects to choose for a task, that requires recognition of the chosen object, in a variety of contexts, by comparing attributes which contain a non trivial amount of context dependent variability. We defined that uniqueness assessment as an algorithm that computes a fuzzy set membership function that measures some but not all aspects of the probability that the sought after object will not be confused with other objects in the space being searched. We evaluated the usefulness of that concept by experimentally assessing the extent to which the uniqueness of the SAD global minimum of locally computed image subset dissimilarity was both a predictor of bidirectional match compliance with the Epipolar Constraint, and a predictor of bidirectional match disparity correctness, for the classical stereoscopic correspondence problem of computer vision, and in that context found the uniqueness of the aforementioned global minimum to be a useful but imperfect predictor of success. In this paper we compare the usefulness of the uniqueness of the aforementioned global minimum to that of, the magnitude of that same global minimum, the magnitude of variability across contributors to that global minimum, uniqueness of that variability, and co-occurrence of the global minimum of local image subset dissimilarity and global minimum of variability across contributors to local image subset dissimilarity.

Paper Details

Date Published: 7 May 2012
PDF: 28 pages
Proc. SPIE 8399, Visual Information Processing XXI, 83990S (7 May 2012); doi: 10.1117/12.918893
Show Author Affiliations
Val Petran, Artificial Perception Technologies Inc. (United States)
Case Western Reserve Univ. (United States)
Frank Merat, Case Western Reserve Univ. (United States)

Published in SPIE Proceedings Vol. 8399:
Visual Information Processing XXI
Mark Allen Neifeld; Amit Ashok, Editor(s)

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