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Multiscale Texture Orientation Analysis Using Spectral Total-Variation Decomposition

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Scale Space and Variational Methods in Computer Vision (SSVM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9087))

Abstract

Multi-level texture separation can considerably improve texture analysis, a significant component in many computer vision tasks. This paper aims at obtaining precise local texture orientations of images in a multiscale manner, characterizing the main obvious ones as well as the very subtle ones. We use the total variation spectral framework to decompose the image into its different textural scales. Gabor filter banks are then employed to detect prominent orientations within the multis-cale representation. A necessary condition for perfect texture separation is given, based on the spectral total-variation theory. We show that using this method we can detect and differentiate a mixture of overlapping textures and obtain with high fidelity a multi-valued orientation representation of the image.

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Authors and Affiliations

  1. Department of Electrical Engineering, Technion - Israel Institute of Technology, 32000, Haifa, Israel

    Dikla Horesh & Guy Gilboa

Authors
  1. Dikla Horesh
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  2. Guy Gilboa
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Corresponding author

Correspondence to Dikla Horesh .

Editor information

Editors and Affiliations

  1. University of Bordeaux, Talence, France

    Jean-François Aujol

  2. ENS Cachan, Cachan, France

    Mila Nikolova

  3. University of Bordeaux, Talence, France

    Nicolas Papadakis

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© 2015 Springer International Publishing Switzerland

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Horesh, D., Gilboa, G. (2015). Multiscale Texture Orientation Analysis Using Spectral Total-Variation Decomposition. In: Aujol, JF., Nikolova, M., Papadakis, N. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2015. Lecture Notes in Computer Science(), vol 9087. Springer, Cham. https://doi.org/10.1007/978-3-319-18461-6_39

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  • DOI: https://doi.org/10.1007/978-3-319-18461-6_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18460-9

  • Online ISBN: 978-3-319-18461-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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