-
A Hierarchical Markov Random Field Model for Figure-ground Segregation
- Stella X. YU, Tai Sing LEE and Takeo KANADE
Lecture Notes in Computer Science 2134, pp. 118-133.
Third International Workshop on
Energy Minimization Methods in Computer Vision and Pattern Recognition
(
EMMCVPR'01
),
INRIASophia-Antipolis, France, September 3 - 5, 2001.
- Abstract
-
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 and global representations, as well as to reason
about the rationales underlying some of recent psychological and
neurophysiological findings related to figure-ground segregation.
- Keywords
-
Markov Random Fields, figure-ground, depth segregation, learning from rehearsals
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