Name
Abstract | ||
|---|---|---|
Boundary detection is a fundamental problem in computer vision. However, bound- ary detection is difficult as it involves integrating multiple cues (intensity, color, texture) as well as trying to incorporate object class or scene level descriptions to mitigate the am- biguity of the local signal. In this paper we investigate incorporating a priori information into boundary detection. We learn a probabilistic model that describes a prior for object boundaries over small patches of the image. We then incorporate this boundary model into a mixture of multiscale conditional random fields, where the mixture components represent different contexts formed by clustering overall spatial distributions of bound- aries across images and image regions (vistas). We demonstrate this approach using challenging real-world road scenes. Importantly, we show that recent spectral methods that have been used in state-of-the-art boundary detection algorithms do not generalize well to these complex scenes. We show that our algorithm successfully learns these boundary distributions and can exploit this knowledge to improve state-of-the-art bound- ary detectors. |
Year | Venue | Keywords |
|---|---|---|
2009 | BMVC | conditional random field,probabilistic model,computer vision,spectral method |
Field | DocType | Citations |
Conditional random field,Computer vision,Pattern recognition,Computer science,A priori and a posteriori,Statistical model,Artificial intelligence,Spectral method,Prior probability,Cluster analysis,Ambiguity,Detector | Conference | 0 |
PageRank | References | Authors |
0.34 | 22 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jonathan Warrell | 1 | 494 | 18.95 |
| Alastair P. Moore | 2 | 59 | 4.07 |
| Simon Prince | 3 | 914 | 60.61 |