Statistics of Pose and Shape in Multi-object Complexes Using Principal Geodesic Analysis

Abstract. A main focus of statistical shape analysis is the description of variability of a population of geometric objects. In this paper, we present work in progress towards modeling the shape and pose variability of sets of multiple objects. Principal geodesic analysis (PGA) is the extension of the standard technique of principal component analysis (PCA) into the nonlinear Riemannian symmetric space of pose and our medial m-rep shape description, a space in which use of PCA would be incorrect. In this paper, we discuss the decoupling of pose and shape in multi-object sets using different normalization settings. Further, we introduce new methods of describing the statistics of object pose using a novel extension of PGA, which previously has been used for global shape statistics. These new pose statistics are then combined with shape statistics to form a more complete description of multi-object complexes. We demonstrate our methods in an application to a longitudinal pediatric autism...

• Type: Conference
• Year: 2006
• Where: MIAR
• Authors: Martin Styner, Kevin Gorczowski, P. Thomas Fletcher, Ja-Yeon Jeong, Stephen M. Pizer, Guido Gerig
• Keywords: MIAR 2006, Shape Analysis, Shape Statistics, Statistical Shape Analysis