Object recognition using neurocomputing and conformal computing geometry

In this paper we present an object recognition technique based on neural computing and projective invariants but now using an omnidirectional vision system and conformal geometric algebra. We also show how to recover the projective invariants from a catadioptric image, where the projective invariants do not hold. With these invariants we train a multilayer perceptron (MLP) neural network to recognize objects.