Name
Title | ||
|---|---|---|
A probabilistic approach for foreground and shadow segmentation in monocular image sequences |
Abstract | ||
|---|---|---|
This paper presents a novel method of foreground and shadow segmentation in monocular indoor image sequences. The models of background, edge information, and shadow are set up and adaptively updated. A Bayesian network is proposed to describe the relationships among the segmentation label, background, intensity, and edge information. A maximum a posteriori-Markov random field estimation is used to boost the spatial connectivity of segmented regions. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1016/j.patcog.2005.02.006 | Pattern Recognition |
Keywords | Field | DocType |
novel method,edge information,bayesian network,shadow segmentation,spatial connectivity,segmented region,posteriori-markov random field estimation,segmentation label,probabilistic approach,monocular image sequence,monocular indoor image sequence,graphical model,pattern recognition | Scale-space segmentation,Markov random field,Image segmentation,Artificial intelligence,Monocular,Computer vision,Shadow,Pattern recognition,Segmentation,Bayesian network,Graphical model,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
38 | 11 | Pattern Recognition |
Citations | PageRank | References |
15 | 1.12 | 18 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Wang | 1 | 948 | 155.42 |
| Tele Tan | 2 | 173 | 28.33 |
| Kia-Fock Loe | 3 | 180 | 20.88 |
| Jiankang Wu | 4 | 576 | 79.80 |