Paper Info
Title
Stereo random field for bi-layer image segmentation
Abstract
Stereo image segmentation usually incorporates depth cues to achieve high quality. However, previous methods that pointwise propagate information within stereo pairs could suffer from a poorly estimated depth map. In this paper, we introduce a novel graphical model where a greater amount of reliable messages can be conveyed during two-view joint segmentation. This model leads to a strongly coupled stereo pair, thus improving robustness, accuracy and consistency of stereo segmentation. Additionally, we augment a depth map to a novel correspondence matrix which is suitable for the proposed stereo segmentation model. Our experiments on a public stereo dataset show that the proposed correspondence method and stereo model outperforms state-of-the-art stereo segmentation algorithms.
Year
DOI
Venue
2013
10.1109/ICME.2013.6607457
ICME
Keywords
Field
DocType
depth map,bi-layer segmentation,random processes,bilayer image segmentation,correspondence matrix,public stereo dataset,image segmentation,matrix algebra,two-view joint segmentation,graphical model,stereo random field,random field,stereo,stereo image segmentation,depth cues,stereo image processing,accuracy,robustness
Computer vision,Scale-space segmentation,Pattern recognition,Segmentation,Computer science,Robustness (computer science),Image segmentation,Artificial intelligence,Graphical model,Depth map,Pointwise,Computer stereo vision
Conference
ISSN
Citations 
PageRank 
1945-7871
0
0.34
References 
Authors
11
2
Name
Order
Citations
PageRank
Kuo-Chin Lien1956.46
Jerry D. Gibson233848.12