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A complete and extendable approach to visual recognition

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3 Author(s)
R. M. Bolle ; IBM T.J. Watson Res. Center, Yorktown Heights, NY, USA ; A. Califano ; R. Kjeldsen

A framework for 3D object recognition is presented. Its flexibility and extensibility are accomplished through a uniform, parallel, and modular recognition architecture. Concurrent and stacked parameter transforms reconstruct a variety of features from the input scene. At each stage, constraint satisfaction networks collect and fuse the evidence obtained through the parameter transforms, ensuring a globally consistent interpretation of the input scene and allowing for the integration of diverse types of information. The final interpretation of the scene is a small consistent subset of the many initial hypotheses about partial features, primitive features, feature assemblies, and 3D objects computed by the various parameter transforms. A complete, integrated, and implemented system that extracts planar surfaces, patches of quadrics of revolution, and planar intersection curves of these surfaces from a depth map viewing 3D objects is described. Experimental results on the recognition behavior of the system are presented

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:14 ,  Issue: 5 )