Classification of Range Surface Type Using Optically Generated Derivative Estimates

We describe a technique to classify surface types from range data using local derivative estimates. We propose an optical architecture using acousto-optic devices to efficiently compute these derivatives. The derivative estimates are combined into curvature functions which are scale-, translation-, and rotation-invariant, and the surface types are determined from these curvature features. Results are presented for the classification of test range surfaces.