Ground Plane Obstacle Detection of Stereo Vision under Variable Camera Geometry Using Neural Nets
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چکیده
We use a stereo disparity predictor, implemented as layered neural nets in the PILUT architecture, to encode the disparity flow field for the ground plane at various viewing positions over the work space. A deviation of disparity, computed using a correspondence algorithm, from its prediction may then indicate a potential obstacle. A casual bayes net model is used to estimate the probability that a point of interest lies on the ground plane.
منابع مشابه
Ground Plane Obstacle Detection Under Variable Camera Geometry Using a Predictive Stereo Matcher
A scheme is proposed for ground plane obstacle detection under conditions of variable camera geometry. It uses a predictive stereo matcher implemented in the PILUT architecture described below, in which is encoded the disparity map of the ground plane for the different viewing positions required to scan the work space. The research is the extension of Mallot et al's (1989) scheme for ground pla...
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تاریخ انتشار 1995