Shape priors attempt to represent biological variations within a population. When variations are global, Principal Component Analysis (PCA) can be used to learn major modes of vari...
Delphine Nain, Steven Haker, Aaron F. Bobick, Alle...
In this paper we address the problem of registering 3D scattered data by the mean of a statistical shape model. This model is built from a training set on which a principal compon...
Statistical shape analysis techniques commonly employed in the medical imaging community, such as Active Shape Models or Active Appearance Models, rely on Principal Component Anal...
Mauricio Reyes, Marius George Linguraru, Kostas Ma...
This paper presents a novel segmentation approach featuring shape constraints of multiple structures. A framework is developed combining statistical shape modeling with a maximum a...
Kilian M. Pohl, Simon K. Warfield, Ron Kikinis, W....
In this paper, a multilinear formulation of the popular Principal Component Analysis (PCA) is proposed, named as multilinear PCA (MPCA), where the input can be not only vectors, b...
Anastasios N. Venetsanopoulos, Haiping Lu, Konstan...