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.
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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