Restructuring aspect graphs into aspect- and cell-equivalence classes for use in computer vision
A potential disadvantage of using aspect graphs as object models in a computer vision system is their large size. The upper bound on the number of nodes in the aspect graph of an N-face convex object is O(N⋆⋆3). In this paper we introduce the concepts of "aspect-equivalence" and "cell-equivalence" and present a method of using them to restructure a data base of aspect graphs. This process identifies an interconnected set of equivalence classes which we use to form an Equivalence Class Graph (ECG).
KeywordsObject Recognition Object Model Cell Boundary Longe Edge Robot Vision
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