Towards a coherent statistical framework for dense deformable template estimation

• 成果类型: Article
• 署名作者: Allassonniere, S.; Amit, Y.; Trouve, A.
• 署名单位: University of Chicago; Universite Paris 13; Universite Paris Saclay
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• 发表日期: 2007
• 页码: 3-29
• 关键词: metrics

摘要

The problem of estimating probabilistic deformable template models in the field of computer vision or of probabilistic atlases in the field of computational anatomy has not yet received a coherent statistical formulation and remains a challenge. We provide a careful definition and analysis of a well-defined statistical model based on dense deformable templates for grey level images of deformable objects. We propose a rigorous Bayesian framework for which we prove asymptotic consistency of the maximum a posteriori estimate and which leads to an effective iterative estimation algorithm of the geometric and photometric parameters in the small sample setting. The model is extended to mixtures of finite numbers of such components leading to a fine description of the photometric and geometric variations of an object class. We illustrate some of the ideas with images of handwritten digits and apply the estimated models to classification through maximum likelihood.