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Adaptation of Tensor Voting to Image Structure Estimation

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New Developments in the Visualization and Processing of Tensor Fields

Part of the book series: Mathematics and Visualization ((MATHVISUAL))

Abstract

Tensor voting is a well-known robust technique for extracting perceptual information from clouds of points. This chapter proposes a general methodology to adapt tensor voting to different types of images in the specific context of image structure estimation. This methodology is based on the structural relationships between tensor voting and the so-called structure tensor, which is the most popular technique for image structure estimation. The problematic Gaussian convolution used by the structure tensor is replaced by tensor voting. Afterwards, the results are appropriately rescaled. This methodology is adapted to gray-valued, color, vector- and tensor-valued images. Results show that tensor voting can estimate image structure more appropriately than the structure tensor and also more robustly.

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Acknowledgements

This research has been partially supported by the Spanish Ministry of Science and Technology under project DPI2007-66556-C03-03, by the Commissioner for Universities and Research of the Department of Innovation, Universities and Companies of the Catalonian Government and by the European Social Fund.

Author information

Authors and Affiliations

  1. Department of Medical and Health Sciences (IMH), Linköping University, Center for Medical Image Science and Visualization (CMIV), Campus US, 58185, Linköping, Sweden

    Rodrigo Moreno

  2. Department of Computing, Imperial College London, 180 Queen’s Gate, SW7 2AZ, London, UK

    Luis Pizarro

  3. Faculty of Mathematics and Computer Science, Saarland University, Building E2.4, 66041, Saarbrücken, Germany

    Bernhard Burgeth

  4. Faculty of Mathematics and Computer Science, Saarland University, Mathematical Image Analysis Group (MIA), Building E1.1, 66041, Saarbrücken, Germany

    Joachim Weickert

  5. Department of Electronic and Communications Technology, Autonomous University of Madrid, Francisco Tomas y Valiente 11, 28049, Madrid, Spain

    Miguel Angel Garcia

  6. Department of Computer Science and Mathematics, Intelligent Robotics and Computer Vision Group (IRCV), Rovira i Virgili University, Av. Països Catalans 26, 43007, Tarragona, Spain

    Domenec Puig

Authors
  1. Rodrigo Moreno
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  2. Luis Pizarro
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  3. Bernhard Burgeth
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  4. Joachim Weickert
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  5. Miguel Angel Garcia
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  6. Domenec Puig
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Corresponding author

Correspondence to Rodrigo Moreno .

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Editors and Affiliations

  1. Dept. Computer Science, Brown University, Providence, Rhode Island, USA

    David H. Laidlaw

  2. Department of Biomedical Engineering, Einhoven University of Technology, Eindhoven, 5600 MB, Netherlands

    Anna Vilanova

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Moreno, R., Pizarro, L., Burgeth, B., Weickert, J., Garcia, M.A., Puig, D. (2012). Adaptation of Tensor Voting to Image Structure Estimation. In: Laidlaw, D., Vilanova, A. (eds) New Developments in the Visualization and Processing of Tensor Fields. Mathematics and Visualization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27343-8_2

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