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International Symposium on Ubiquitous Networking

UNet 2016: Advances in Ubiquitous Networking 2 pp 519–527Cite as

Texture Segmentation Based on Dual Tree Complex Wavelet Transform and Support Vector Machine

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Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 397))

Abstract

This paper presents a new approach for segmentation of the textured images that exploits properties of the dual-tree complex wavelet transform, shift invariance and six directional sub-bands at each scale, and uses a feature vector comprising of mean and standard deviation of the six directional sub-bands over a sliding window. The classification of each sliding window using Support Vector Machine (SVM) leads to a segmented image. Through experiments on a variety of synthetic images of texture data sets, we show that our algorithm yields significant performance improvements for texture segmentation, as compared with other state-of-the-art methods of feature extraction.

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Author information

Authors and Affiliations

  1. INPT, Rabat, Morocco

    Amal Farress & Ahmed Tamtaoui

  2. INSEA, Rabat, Morocco

    Mohamed Nabil Saidi

Authors
  1. Amal Farress
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  2. Mohamed Nabil Saidi
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  3. Ahmed Tamtaoui
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Corresponding author

Correspondence to Amal Farress .

Editor information

Editors and Affiliations

  1. Computer Science Laboratory (LIA), University of Avignon, Avignon, France

    Rachid El-Azouzi

  2. Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

    Daniel Sadoc Menasche

  3. Hassan II University of Casablanca, ENSEM, Casablanca, Morocco

    Essaïd Sabir

  4. CREATE-NET, Trento, Italy

    Francesco De Pellegrini

  5. INPT, Rabat, Morocco

    Mustapha Benjillali

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© 2017 Springer Science+Business Media Singapore

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Farress, A., Saidi, M.N., Tamtaoui, A. (2017). Texture Segmentation Based on Dual Tree Complex Wavelet Transform and Support Vector Machine. In: El-Azouzi, R., Menasche, D.S., Sabir, E., De Pellegrini, F., Benjillali, M. (eds) Advances in Ubiquitous Networking 2. UNet 2016. Lecture Notes in Electrical Engineering, vol 397. Springer, Singapore. https://doi.org/10.1007/978-981-10-1627-1_41

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  • DOI: https://doi.org/10.1007/978-981-10-1627-1_41

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-1626-4

  • Online ISBN: 978-981-10-1627-1

  • eBook Packages: EngineeringEngineering (R0)

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