Sciweavers

Share
EMMCVPR
2001
Springer

A Hierarchical Markov Random Field Model for Figure-Ground Segregation

9 years 6 months ago
A Hierarchical Markov Random Field Model for Figure-Ground Segregation
To segregate overlapping objects into depth layers requires the integration of local occlusion cues distributed over the entire image into a global percept. We propose to model this process using hierarchical Markov random field (HMRF), and suggest a broader view that clique potentials in MRF models can be used to encode any local decision rules. A topology-dependent multiscale hierarchy is used to introduce long range interaction. The operations within each level are identical across the hierarchy. The clique parameters that encode the relative importance of these decision rules are estimated using an optimization technique called learning from rehearsals based on 2-object training samples. We find that this model generalizes successfully to 5-object test images, and that depth segregation can be completed within two traversals across the hierarchy. This computational framework therefore provides an interesting platform for us to investigate the interaction of local decision rules a...
Stella X. Yu, Tai Sing Lee, Takeo Kanade
Added 28 Jul 2010
Updated 28 Jul 2010
Type Conference
Year 2001
Where EMMCVPR
Authors Stella X. Yu, Tai Sing Lee, Takeo Kanade
Comments (0)
books