Name
Abstract | ||
|---|---|---|
In this paper, we propose and integrate two Bayesian methods, one of them for junction detection, and the other one for junction grouping. Our junction detection method relies on a probabilistic edge model and a log-likelihood test. Our junction grouping method relies on finding connecting paths between pairs of junctions. Path searching is performed by applying a Bayesian A∗ algorithm. Such algorithm uses both an intensity and geometric model for defining the rewards of a partial path and prunes those paths with low rewards. We have extended such a pruning with an additional rule which favors the stability of longer paths against shorter ones. We have tested experimentally the efficiency and robustness of the methods in an indoor image sequence. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1016/S0031-3203(01)00150-9 | Pattern Recognition |
Keywords | Field | DocType |
Junctions detection,Grouping,Image segmentation,Bayesian inference | Bayesian inference,Pattern recognition,Geometric modeling,Robustness (computer science),Image segmentation,Edge model,Artificial intelligence,Probabilistic logic,Image sequence,Machine learning,Mathematics,Bayesian probability | Journal |
Volume | Issue | ISSN |
35 | 9 | 0031-3203 |
Citations | PageRank | References |
7 | 0.53 | 17 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Miguel Cazorla | 1 | 325 | 44.17 |
| Francisco Escolano | 2 | 532 | 46.61 |
| Domingo Gallardo | 3 | 25 | 3.92 |
| R. Rizo | 4 | 51 | 14.90 |