CASIA OpenIR  > 智能制造技术与系统研究中心  > 多维数据分析
A sampling-based GEM algorithm with classification for texture synthesis
Lai Liu-Yuan; Hwang Wen-Liang; Peng Silong; Liu-yuan Lai
2006
Conference Name2006 IEEE International Conference on Acoustics Speech and Signal Processing ICASSP 2006
Pagespp 769-772
Conference Date2006/5/14-2006/5/19
Conference PlaceFranceToulouse France
AbstractResearch 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.
Keyword
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12891
Collection智能制造技术与系统研究中心_多维数据分析
Corresponding AuthorLiu-yuan Lai
Recommended Citation
GB/T 7714
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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