Population Shape Regression From Random Design Data

Regression analysis is a powerful tool for the study of changes in a dependent variable as a function of an independent regressor variable, and in particular it is applicable to the study of anatomical growth and shape change. When the underlying process can be modeled by parameters in a Euclidean space, classical regression techniques [13, 34] are applicable and have been studied extensively. However, recent work suggests that attempts to describe anatomical shapes using flat Euclidean spaces undermines our ability to represent natural biological variability [9, 11]. In this paper we develop a method for regression analysis of general, manifold-valued data. Specifically, we extend Nadaraya-Watson kernel regression by recasting the regression problem in terms of Fr?echet expectation. Although this method is quite general, our driving problem is the study anatomical shape change as a function of age from random design image data. We demonstrate our method by analyzing shape change in t...

• Type: Conference
• Year: 2007
• Where: ICCV
• Authors: Bradley C. Davis, P. Thomas Fletcher, Elizabeth Bullitt, Sarang C. Joshi
• Keywords: Classical Regression Techniques, Computer Vision, ICCV 2007, Nadaraya-Watson Kernel Regression, Representative Anatomical Shape, Shape Change, Study Anatomical Shape