Abstract

Abstract Techniques are presented for identifying unoccluded three‐dimensional objects from arbitrary viewing angles, in the framework of a model‐based feature vector classification scheme. Fourier descriptors and moments are used for feature vector generation from, respectively, contour imagery, and silhouette and/or range imagery. A class of objects, Airplanes, is defined with six distinct example types in our test data set. An additional data set of four objects from outside this class is also defined. A method for generating an exhaustive set of library views, and worst case test views has been developed, using a polyhedral approximation to a sphere. Based on matching to this library, object class membership, type, and orientation is determined. An approach called classification quality assessment (CQA) is applied to this recognition paradigm to both assess and deal with uncertainty. This is a two level process: the first rejects objects that are not members of a known class, and hence not contained i...

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