title: Elastic geodesic paths in shape space of parameterized surfaces.
abstract: This paper presents a novel Riemannian framework for shape analysis of parameterized surfaces. In particular, it provides efficient algorithms for computing geodesic paths which, in turn, are important for comparing, matching, and deforming surfaces. The novelty of this framework is that geodesics are invariant to the parameterizations of surfaces and other shape-preserving transformations of surfaces. The basic idea is to formulate a space of embedded surfaces (surfaces seen as embeddings of a unit sphere in IR3) and impose a Riemannian metric on it in such a way that the reparameterization group acts on this space by isometries. Under this framework, we solve two optimization problems. One, given any two surfaces at arbitrary rotations and parameterizations, we use a path-straightening approach to find a geodesic path between them under the chosen metric. Second, by modifying a technique presented in [25], we solve for the optimal rotation and parameterization (registration) between surfaces. Their combined solution provides an efficient mechanism for computing geodesic paths in shape spaces of parameterized surfaces. We illustrate these ideas using examples from shape analysis of anatomical structures and other general surfaces.
authors.affiliation: Department of Statistics, Florida State University, 117 N. Woodward Ave., PO Box 3064330, Tallahassee, FL 32306, USA. skurtek@stat.fsu.edu
authors.forename: Sebastian
authors.initials: S
authors.lastname: Kurtek
authors.forename: Eric
authors.initials: E
authors.lastname: Klassen
authors.forename: John C
authors.initials: JC
authors.lastname: Gore
authors.forename: Zhaohua
authors.initials: Z
authors.lastname: Ding
authors.forename: Anuj
authors.initials: A
authors.lastname: Srivastava
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D010363:Pattern Recognition, Automated / Q000379:methods*