|A sampling-based GEM algorithm with classification for texture synthesis|
|Lai Liu-Yuan; Hwang Wen-Liang; Peng Silong; Liu-yuan Lai
|Conference Name||2006 IEEE International Conference on Acoustics Speech and Signal Processing ICASSP 2006
|Conference Place||FranceToulouse France
|Abstract||Research on texture synthesis has made substantial progress in recent years and many patch-based sampling algorithms now produce quality results in an acceptable computation time. However when such algorithms are applied whether they provide good
results for specific textures and why they do so are questions
that have yet to be answered. In this article we deal specifically
with the second question by modeling the synthesis problem as
one of learning from incomplete data and propose an algorithm
that is a generalization of patch-work approach. Through this
algorithm we demonstrate that the solution of patch-based sampling approaches is an approximation of finding the maximumlikelihood optimum by the generalized expectation and maximization (GEM) algorithm.|
|Corresponding Author||Liu-yuan Lai|
Lai Liu-Yuan,Hwang Wen-Liang,Peng Silong,et al. A sampling-based GEM algorithm with classification for texture synthesis[C],2006:pp 769-772.
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