Optimal training set design for 3D object recognition

We describe a general approach for the representation and recognition of 3D objects. The method is based on a novel view selection mechanism that develops "visual filters" responsive to specific object classes to encode the complete viewing sphere with a small number of prototypical examples. The optimal set of visual filters is found via a cross-validation-like data reduction algorithm used to train banks of back propagation (BP) neural networks. Experimental results on real-world imagery demonstrate the feasibility of our approach.