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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.doi:10.5244/c.9.22 dblp:conf/bmvc/ShaoMH95 fatcat:yozuwmbze5b7pj7yidw2l75doi