<title>Multiple-constraints neural network solution for edge-pixel-based stereo correspondence problem</title>

Abstract

This paper describes a fast and robust artificial neural network algorithm for solving the stereo correspondence problem in binocular vision. In this algorithm, the stereo correspondence problem is modelled as a cost minimization problem where the cost is the value of the matching function between the edge pixels along the same epipolar line. A multiple-constraint energy minimization neural network is implemented for this matching process. This algorithm differs from previous works in that it integrates ordering and geometry constraints in addition to uniqueness, continuity, and epipolar line constraint into a neural network implementation. The processing procedures are similar to that of the human vision processes. The edge pixels are divided into different clusters according to their orientation and contrast polarity. The matching is performed only between the edge pixels in the same clusters and at the same epipolar line. By following the epipolar line, the ordering constraint (the left-right relation between pixels) can be specified easily without building extra relational graphs as in the earlier works. The algorithm thus assigns artificial neurons which follow the same order of the pixels along an epipolar line to represent the matching candidate pairs. The algorithm is discussed in detail and experimental results using real images are presented.