Fast Algorithms for Low-Level Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A neural matching algorithm for 3-D reconstruction from stereo pairs of linear images
Pattern Recognition Letters - Special issue on neural networks for computer vision applications
ACM Computing Surveys (CSUR)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting and localising obstacles in front of a moving vehicle using linear stereo vision
Mathematical and Computer Modelling: An International Journal
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A neural implementation for achieving real-time obstacle detection in front of a moving vehicle using a linear stereoscopic sensor is presented. The key problem is the so-called ''correspondence problem'' which consists in matching features in two stereo images that are projections of the same physical entity in the three-dimensional world. In our approach, the set of edge points extracted from each linear image is first split into two classes. Within each of these classes, the matching problem is turned into an optimization task where an energy function, which represents the constraints on the solution, is to be minimized. The optimization problem is then performed thanks to an analog Hopfield neural network. The preliminary discrimination of the edge points allows us to implement the matching process as two networks running in parallel. Experimental results are presented to demonstrate the effectiveness of the approach for 3-D reconstruction in real traffic conditions.