Name
Title | ||
|---|---|---|
Ground plane obstacle detection of stereo vision under variable camera geometry using neural nets |
Abstract | ||
|---|---|---|
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. |
Year | Venue | Keywords |
|---|---|---|
1995 | BMVC | ground plane obstacle detection,stereo vision,variable camera geometry,neural net |
Field | DocType | ISBN |
Computer vision,Obstacle,Computer science,Stereopsis,Ground plane,Bayesian network,Artificial intelligence,Point of interest,Artificial neural network,Geometry | Conference | 0-9521898-2-8 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Y. Shao | 1 | 0 | 0.34 |
| J. E. W. Mayhew | 2 | 97 | 89.03 |
| S. D. Hippisley-Cox | 3 | 3 | 1.00 |