Large-scale tests of a keyed, appearance-based 3-D object recognition system.
We describe and analyze an appearance-based 3-D object recognition system that avoids some of the problems of previous appearance-based schemes. We describe various large-scale performance tests and report good performance for full-sphere/hemisphere recognition of up to 24 complex, curved objects, robustness against clutter and occlusion, and some intriguing generic recognition behavior. We also establish a protocol that permits performance in the presence of quantifiable amounts of clutter and occlusion to be predicted on the basis of simple score statistics derived from clean test images and pure clutter images.
Nelson
R C
RC
Department of Computer Science, University of Rochester, NY 14627, USA. nelson@rochester.edu
Selinger
A
A
D003198:Computer Simulation; D003867:Depth Perception / Q000502:physiology*; D005556:Form Perception / Q000502:physiology*; D006801:Humans; D007858:Learning; D015233:Models, Statistical; D011601:Psychophysics