Name
Abstract | ||
|---|---|---|
Template matching is an effective means of locating vehicles in outdoor scenes, but it tends to be a computationally expensive. To reduce processing time, we use large neural networks to predict, or index, a small subset of templates that are likely to match each window in an image. Results on actual LADAR range images show that limiting the templates to those selected by the neural networks reduces the computation time by a factor of 5 without sacrificing the accuracy of the results. |
Year | DOI | Venue |
|---|---|---|
1997 | 10.1109/ICNN.1997.614009 | 1997 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS, VOLS 1-4 |
Keywords | Field | DocType |
laser radar,indexation,neural nets,computer networks,data mining,image sensors,layout,computational complexity,neural networks,object recognition,indexing,template matching,neural network | Template matching,Computer vision,Large networks,Pattern recognition,Computer science,Search engine indexing,Artificial intelligence,Template,Artificial neural network,Cognitive neuroscience of visual object recognition,Computational complexity theory,Computation | Conference |
Citations | PageRank | References |
2 | 0.41 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mark R. Stevens | 1 | 75 | 8.93 |
| Charles W. Anderson | 2 | 1061 | 521.95 |
| J. Ross Beveridge | 3 | 6 | 1.25 |