Improving dynamic programming to solve image registration
Pattern Recognition
Surfaces from Stereo: Integrating Feature Matching, Disparity Estimation, and Contour Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Point Correspondence in the Presence of Occlusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching Aerial Images to 3-D Terrain Maps
IEEE Transactions on Pattern Analysis and Machine Intelligence
Relaxation by the Hopfield neural network
Pattern Recognition
A Theory of Human Stereo Vision
A Theory of Human Stereo Vision
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In this paper, a neural network based optimization method is described in order to solve the problem of stereo matching for a set of primitives extracted from a stereoscopic pair of images. The neural network used is the 2D Hopfield network. The matching problem amounts to the minimization of an energy function involving specified stereoscopic constraints. This function reaches its minimum when these constraints are satisfied. The network converges to its stable state when the minimum is reached. In the initial step, the primitives to match are extracted from the stereoscopic pair of images. The primitives we use are specific points of interest. The feature extraction technique is the one developed by Moravec, and called the interest operator. Its output comprises mostly corners or feature points with high variance. The Hopfield network is represented as a Nl × Nr matrix of neurons, where Nl is the number of features in the left image and Nr the number of features in the right one. An update of the state of each neuron is done in order to perform the network evolution and then allowing it to settle down into a stable state. In the stable state, each neuron represents a possible match between a left candidate and a right one.