Performance Analysis of Cooperative Hopfield Networks for Stereo Matching
Computational Intelligence and Security
Critical motion detection of nearby moving vehicles in a vision-based driver-assistance system
IEEE Transactions on Intelligent Transportation Systems
Efficient multisensory barrier for obstacle detection on railways
IEEE Transactions on Intelligent Transportation Systems
Data segmentation of stereo images with complicated background
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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The focus of this paper is on real-time obstacle detection using linear stereo vision. This paper presents a multilevel neural method for matching edges extracted from stereo linear images. The method described performs edge stereo matching at different levels with a neural-network-based procedure. At each level, the process starts by selecting, in the left and right linear images, the most significant edges, i.e., those with the largest gradient magnitudes. The selected edges are then matched and the obtained pairs are used as reference pairs for matching less significant edges in the next level. In each level, the matching problem is formulated as an optimization task in which an objective function, representing the constraints on the solution, is minimized thanks to a Hopfield neural network.