Nonparametric Bayesian Texture Learning and Synthesis

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...

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Bibliographic Details
Main Authors: Zhu, Long, Chen, Yuanhao, Freeman, William T., Torralba, Antonio
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Format: Article
Language:en_US
Published: Neural Information Processing Systems Foundation 2011
Online Access:http://hdl.handle.net/1721.1/64454
https://orcid.org/0000-0002-2231-7995
https://orcid.org/0000-0003-4915-0256

Internet

http://hdl.handle.net/1721.1/64454
https://orcid.org/0000-0002-2231-7995
https://orcid.org/0000-0003-4915-0256

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