3D object recognition in range images using hidden markov models and neural networks

This paper presents a technique for obtaining the distance of a step edge from the lens of a camera using a single defocused image of that edge. The proposed technique does not suffer from the correspondence and occlusion problems associated with methods based on multiple images. The technique employs a Multi-Layer Perceptron network trained by backpropagation to compute distances from derivative images of blurred edges. The paper gives experimental results which clearly show the accuracy of the proposed technique. (C) 1999 Published by Elsevier Science Ltd on behalf of the Pattern Recognition Society. All rights reserved.