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Amoeba Active Contours

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Scale Space and Variational Methods in Computer Vision (SSVM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6667))

Abstract

We introduce an algorithm for active contour segmentation in which the level set function encoding the contour is processed by median filtering using morphological amoebas. These are adaptive structure elements introduced by Lerallut et al. which can be combined with different morphological operations. Recently it has been proven that iterated amoeba median filtering of an image approximates the well-known self-snakes partial differential equation. Following this approach we prove a partial approximation property of amoeba active contours with respect to geodesic active contours. Experiments prove the viability of the algorithm and confirm the theoretical results.

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Author information

Authors and Affiliations

  1. Institute for Biomedical Image Analysis, University for Health Sciences, Medical Informatics and Technology, Eduard-Wallnöfer-Zentrum 1, 6060, Hall/Tyrol, Austria

    Martin Welk

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  1. Martin Welk
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Editor information

Editors and Affiliations

  1. Computer Science Department, Technion — Israel Institute of Technology, 718 Taub Building Technion, 32000, Haifa, Israel

    Alfred M. Bruckstein

  2. Department of Biomedical Engineering, Biomedical Image Analysis and Interpretation, Eindhoven University of Technology, Den Dolech 2 – WH 2.101, 5600 MB, Eindhoven, The Netherlands

    Bart M. ter Haar Romeny

  3. Faculaty of Engineering, School of Electrical Engineering, Tel Aviv University, Ramat Aviv, 69978, Israel

    Alexander M. Bronstein

  4. Institute of Computationa Science, Faculty of Informatics SI-109, Università della Svizzera Italiana, Via Giuseppe Buffi 13, 6904, Lugano, Switzerland

    Michael M. Bronstein

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© 2012 Springer-Verlag Berlin Heidelberg

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Welk, M. (2012). Amoeba Active Contours. In: Bruckstein, A.M., ter Haar Romeny, B.M., Bronstein, A.M., Bronstein, M.M. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2011. Lecture Notes in Computer Science, vol 6667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24785-9_32

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  • DOI: https://doi.org/10.1007/978-3-642-24785-9_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24784-2

  • Online ISBN: 978-3-642-24785-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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