2 research outputs found
Nonparametric Bayesian Texture Learning and Synthesis
We present a nonparametric Bayesian method for texture learning and synthesis.
A texture image is represented by a 2D Hidden Markov Model (2DHMM) where
the hidden states correspond to the cluster labeling of textons and the transition
matrix encodes their spatial layout (the compatibility between adjacent textons).
The 2DHMM is coupled with the Hierarchical Dirichlet process (HDP) which allows
the number of textons and the complexity of transition matrix grow as the
input texture becomes irregular. The HDP makes use of Dirichlet process prior
which favors regular textures by penalizing the model complexity. This framework
(HDP-2DHMM) learns the texton vocabulary and their spatial layout jointly
and automatically. The HDP-2DHMM results in a compact representation of textures
which allows fast texture synthesis with comparable rendering quality over
the state-of-the-art patch-based rendering methods. We also show that the HDP-
2DHMM can be applied to perform image segmentation and synthesis. The preliminary
results suggest that HDP-2DHMM is generally useful for further applications
in low-level vision problems.United States. National Geospatial-Intelligence Agency (NEGI-1582-04- 0004)United States. Office of Naval Research (MURI Grant N00014-06-1-0734)United States. Advanced Research Projects Agency-Energy (VACE-II)Microsoft ResearchGoogle (Firm