Abstract

We discuss an algorithm that enables a machine-vision system to recognize a three-dimensional object from a series of multiple views. The algorithm uses pseudoreflection tomography to map N images of the object into a single signature image that incorporates information from all N views. Rotation-, scale-, and brightness-invariant features are extracted from the signature image that permits robust recognition of the object. The signature image enjoys other invariants as well, such as invariance with respect to both translation of the yaw (i.e., y) axis and slight vertical shifts of the object along the yaw axis. Rotation-invariant features are extracted by using the circular-harmonic functions originally introduced by Hsu et al. [ Appl. Opt. 21, 4012 ( 1982)]. Scale invariance is obtained by using Mellin transforms. If only partial-view data are available, the probability of correct recognition is obviously decreased but can to some extent be ameliorated—at least in a low-noise environment—by using similarity measures between test and reference objects based on gray-level coincidence statistics.

© 1986 Optical Society of America

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