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From a Modified Ambrosio-Tortorelli to a Randomized Part Hierarchy Tree

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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 demonstrate the possibility of coding parts, features that are higher level than boundaries, using a modified AT field after augmenting the interaction term of the AT energy with a non-local term and weakening the separation into boundary/not-boundary phases. The iteratively extracted parts using the level curves with double point singularities are organized as a proper binary tree. Inconsistencies due to non-generic configurations for level curves as well as due to visual changes such as occlusion are successfully handled once the tree is endowed with a probabilistic structure. The work is a step in establishing the AT function as a bridge between low and high level visual processing.

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Authors and Affiliations

  1. Department of Computer Engineering, Middle East Technical University, Ankara, TR-06531, Turkey

    Sibel Tari & Murat Genctav

Authors
  1. Sibel Tari
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  2. Murat Genctav
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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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Tari, S., Genctav, M. (2012). From a Modified Ambrosio-Tortorelli to a Randomized Part Hierarchy Tree. 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_23

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  • DOI: https://doi.org/10.1007/978-3-642-24785-9_23

  • 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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