Bayesian Faces via Hierarchical Template Modeling
作者:
D.B. Phillips,
A.F. M. Smith,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1994)
卷期:
Volume 89,
issue 428
页码: 1151-1163
ISSN:0162-1459
年代: 1994
DOI:10.1080/01621459.1994.10476855
出版商: Taylor & Francis Group
关键词: Deformable template;Facial feature;Hastings-Metropolis algorithm;Hierarchical model;Image analysis;Markov chain Monte Carlo
数据来源: Taylor
摘要:
We consider the problem of directly extracting high-level shape information from images of scenes involving faces. The approach adopted owes much to the work of Grenander and colleagues at Brown University on pattern analysis and involves designing stochastic deformable templates for objects in the underlying image scenes. A wide range of realistic object poses can be captured by imposing a prior probability distribution over the space of allowable deformations. We show how hierarchical models can be used to organize the prior information into a coherent structure. Markov chain Monte Carlo methods are exploited to recover the deformation given observed image data.
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