Journal article
Shape analysis of surfaces using general elastic metrics
Journal of Mathematical Imaging and Vision
2020
Abstract
In this article, we introduce a family of elastic metrics on the space of parametrized surfaces in 3D space using a corresponding family of metrics on the space of vector-valued one-forms. We provide a numerical framework for the computation of geodesics with respect to these metrics. The family of metrics is invariant under rigid motions and reparametrizations; hence, it induces a metric on the “shape space” of surfaces. This new class of metrics generalizes a previously studied family of elastic metrics and includes in particular the Square Root Normal Field (SRNF) metric, which has been proven successful in various applications. We demonstrate our framework by showing several examples of geodesics and compare our results with earlier results obtained from the SRNF framework.
Details
- Title
- Shape analysis of surfaces using general elastic metrics
- Authors/Creators
- Z. Su (Author/Creator) - Florida State UniversityM. Bauer (Author/Creator) - Florida State UniversityS.C. Preston (Author/Creator) - The Graduate Center, CUNYH. Laga (Author/Creator) - Murdoch UniversityE. Klassen (Author/Creator) - Florida State University
- Publication Details
- Journal of Mathematical Imaging and Vision
- Publisher
- Kluwer Academic Publishers
- Identifiers
- 991005545196507891
- Copyright
- © 2020 Springer Nature Switzerland AG.
- Murdoch Affiliation
- Information Technology, Mathematics and Statistics
- Language
- English
- Resource Type
- Journal article
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