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Local Phase Quantization for Blur Insensitive Texture Description

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Local Binary Patterns: New Variants and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 506))

Abstract

Blur is one of the most common sources of image quality degradations, and it appears very often in practical photography. Most often blur is a result of misfocused optics, changes in the camera pose, and movements in the scene. Beyond the impaired visual quality, blurring may cause severe complications to computer vision algorithms, particularly in texture analysis. These problems have been tackled using deblurring approaches, which ultimately leads to much harder intermediate problem versus the original task of texture characterization. In this chapter, we present a simple yet powerful texture descriptor that is, by design, tolerant to most common types of image blurs. The proposed approach is based on quantizing the phase information of the local Fourier transform, which leads to computationally efficient and compact feature representation. We show how to construct several variants of our descriptor including rotation invariance and dynamic texture representation. Moreover, we present texture classification experiments, which illustrate the behavior under several different blur configurations. Surprisingly, the descriptor also achieves state-of-the-art performance with sharp textures, although the main design criteria was tolerance to blur.

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Notes

  1. 1.

    http://www.outex.oulu.fi/

  2. 2.

    Matlab procedure for generating blurred test images is available at http://www.cse.oulu.fi/CMV/Downloads/LPQMatlab.

  3. 3.

    http://www.outex.oulu.fi/

  4. 4.

    http://www.cse.oulu.fi/CMV/Downloads/LBPMatlab

  5. 5.

    http://vision.ece.ucsb.edu/texture/software/

  6. 6.

    http://www.robots.ox.ac.uk/~vgg/research/texclass/

  7. 7.

    http://www.wisdom.weizmann.ac.il/~levina/papers/LevinEtalCVPR09Data.rar

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Acknowledgments

This work was partially supported by Academy of Finland (Grant no. 127702). Authors would like to thank Mr. Veli Juhani Päivärinta for providing his material to this chapter.

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Authors and Affiliations

  1. Center for Machine Vision Research, University of Oulu, 4500, 90014 , Oulu, Finland

    Janne Heikkilä & Esa Rahtu

  2. Institute for Molecular Medicine Finland FIMM, Helsinki, Finland

    Ville Ojansivu

Authors
  1. Janne Heikkilä
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  2. Esa Rahtu
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  3. Ville Ojansivu
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Correspondence to Janne Heikkilä .

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Editors and Affiliations

  1. Computer Information Systems, Missouri State University, Springfield, USA

    Sheryl Brahnam

  2. Faculty of Education, Science, Technology & Mathematics, University of Canberra, Canberra, Australia

    Lakhmi C. Jain

  3. Departimento di Elettronica e Informatica (DEI), Università di Padova, Padua, Italy

    Loris Nanni

  4. DEIS University of Bologna, Cesena, Italy

    Alessandra Lumini

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Heikkilä, J., Rahtu, E., Ojansivu, V. (2014). Local Phase Quantization for Blur Insensitive Texture Description. In: Brahnam, S., Jain, L., Nanni, L., Lumini, A. (eds) Local Binary Patterns: New Variants and Applications. Studies in Computational Intelligence, vol 506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39289-4_3

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  • DOI: https://doi.org/10.1007/978-3-642-39289-4_3

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