Please use this identifier to cite or link to this item: doi:10.22028/D291-26211
Title: Fast methods for implicit active contour models
Author(s): Weickert, Joachim
Kühne, Gerald
Language: English
Year of Publication: 2002
Free key words: computer vision
mean curvature motion
DDC notations: 510 Mathematics
Publikation type: Other
Abstract: Implicit active contour models belong to the most popular level set methods in computer vision. Typical implementations, however, suffer from poor efficiency. In this paper we survey an efficient algorithm that is based on an additive operator splitting (AOS). It is suitable for geometric and geodesic active contour models as well as for mean curvature motion. It uses harmonic averaging and does not require to compute the distance function in each iteration step. We prove that the scheme satisfies a discrete maximum-minimum principle which implies unconditional stability if no balloon forces are present. Moreover, it possesses all typical advantages of AOS schemes: simple implementation, equal treatment of all axes, suitability for parallel computing, and straightforward generalization to higher dimensions. Experiments show that one can gain a speed up by one order of magnitude compared to the widely used explicit time discretization.
Link to this record: urn:nbn:de:bsz:291-scidok-43861
Series name: Preprint / Fachrichtung Mathematik, Universität des Saarlandes
Series volume: 61
Date of registration: 2-Dec-2011
Faculty: MI - Fakultät für Mathematik und Informatik
Department: MI - Mathematik
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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