Improving Appearance-Based Object Recognition in Cluttered Backgrounds

Andrea Selinger and Randal C. Nelson
University of Rochester Department of Computer Science TR 725
January, 2000

Abstract: Appearance-based object recognition systems are currently the most successful approach for dealing with 3D recognition of arbitrary objects in the presence of clutter and occlusion. However, no current system seems directly scalable to human performance levels in this domain.

In this report we describe a series of experiments on a previously described object recognition system that try to see which, if any design axes of such systems hold the greatest potential for improving performance. We look at the potential effect of different design modifications and we conclude that the greatest leverage lies at the level of intermediate feature construction.