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Assistant Professor School of Electrical Engineering and Computer Science, Washington State University
We propose two approaches for utilizing multiple-feature group (triples) and multiple-view information to reduce the number of hypotheses passed to the verification stage in an invariant feature indexing (IFI)-based object recognition system. The first approach is based on a majority voting scheme that keeps track of the number of consistent votes cast by prototype hypotheses for particular object models. The second approach examines the consistency of estimated object pose from multiple scene-triples of a single view or multiple views. Monte Carlo experiments employing 500 single-view synthetic range images and 195 pairs of synthetic range images with a large CAD-based 3D object database show that a significant number of hypotheses can be eliminated by using these approaches. The proposed approaches have also been tested on real range images of several objects. A salient feature of our system and experiment design compared to most existing 3D object recognition systems is our use of a large object data base and a large number of test images.
Host: Yangsheng Xu (email@example.com) Appointment: Lalit Katragadda (firstname.lastname@example.org)