Bayesian Faces via Hierarchical Template Modeling
研究如何从包含人脸的图像中直接提取高层次形状信息，采用随机可变形模板和层次模型组织先验信息，并用马尔可夫链蒙特卡洛方法从观测数据中恢复变形。
Abstract 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.