Variational level set segmentation in Riemannian Sobolev spaces

Baust M, Zikic D, Navab N (2014)

Publication Type: Conference contribution

Publication year: 2014

Publisher: British Machine Vision Association, BMVA

Conference Proceedings Title: BMVC 2014 - Proceedings of the British Machine Vision Conference 2014

Event location: Nottingham, GBR

DOI: 10.5244/c.28.39

Abstract

Gradient flows in the Sobolev space H1 have been shown to enjoy favorable regularity properties. We propose a generalization of prior approaches for Sobolev active contour segmentation by changing the notion of distance in the Sobolev space, which is achieved through treatment of the function and its derivative in Riemannian manifolds. The resulting generalized Riemannian Sobolev space provides the flexibility of choosing an appropriate metric, which can be used to design efficient gradient flows. We select this metric based on the rationale of preconditioning resulting in a significant improvement of convergence and overall runtime in case of variational level set segmentation.

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) Microsoft Corporation US United States (USA) (US)

How to cite

APA:

Baust, M., Zikic, D., & Navab, N. (2014). Variational level set segmentation in Riemannian Sobolev spaces. In Michel Valstar, Andrew French, Tony Pridmore (Eds.), BMVC 2014 - Proceedings of the British Machine Vision Conference 2014. Nottingham, GBR: British Machine Vision Association, BMVA.

MLA:

Baust, Maximilian, Darko Zikic, and Nassir Navab. "Variational level set segmentation in Riemannian Sobolev spaces." Proceedings of the 25th British Machine Vision Conference, BMVC 2014, Nottingham, GBR Ed. Michel Valstar, Andrew French, Tony Pridmore, British Machine Vision Association, BMVA, 2014.

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