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.