A growing network classifier of 3D objects using multiple views

A system for the classification of real 3D objects is presented. Ten objects are presented in arbitrary orientation (and position, within limits). The perception of an object is achieved by the use of multiple stereo pairs of images taken from different view positions. Classification of the spectrum of distances between edge-points perceived on an object is achieved using a constructive algorithm. Convergence to zero errors on the set of training examples is guaranteed. The generalization capability was tested on a set of 10 novel presentations of each object