Ground Plane Obstacle Detection of Stereo Vision under Variable Camera Geometry Using Neural Nets. | Zendy

YuanHai Shao | Zendy; J. E. W. Mayhew | Zendy; SD Hippisley-Cox | Zendy

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Ground Plane Obstacle Detection of Stereo Vision under Variable Camera Geometry Using Neural Nets.

Author(s) -

YuanHai Shao,

J. E. W. Mayhew,

SD Hippisley-Cox

Publication year - 1995

Publication title -

citeseer x (the pennsylvania state university)

Language(s) - English

Resource type - Conference proceedings

DOI - 10.5244/c.9.22

Subject(s) - artificial intelligence , computer vision , obstacle , computer science , stereopsis , ground plane , artificial neural network , encode , plane (geometry) , stereo camera , computer stereo vision , binocular disparity , geometry , mathematics , geography , telecommunications , biochemistry , chemistry , archaeology , antenna (radio) , gene

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.

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