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International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition

EMMCVPR 2015: Energy Minimization Methods in Computer Vision and Pattern Recognition pp 223–236Cite as

A Novel Active Contour Model for Texture Segmentation

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8932))

Abstract

Texture is intuitively defined as a repeated arrangement of a basic pattern or object in an image. There is no mathematical definition of a texture though. The human visual system is able to identify and segment different textures in a given image. Automating this task for a computer is far from trivial.

There are three major components of any texture segmentation algorithm: (a) The features used to represent a texture, (b) the metric induced on this representation space and (c) the clustering algorithm that runs over these features in order to segment a given image into different textures.

In this paper, we propose an active contour based novel unsupervised algorithm for texture segmentation. We use intensity covariance matrices of regions as the defining feature of textures and find regions that have the most inter-region dissimilar covariance matrices using active contours. Since covariance matrices are symmetric positive definite, we use geodesic distance defined on the manifold of symmetric positive definite matrices PD(n) as a measure of dissimilarity between such matrices. Using recent convexification methods, we are able to compute a global maxima of the cost function. We demonstrate performance of our algorithm on both artificial and real texture images.

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Author information

Authors and Affiliations

  1. DA-IICT - Gandhinagar, India

    Aditya Tatu & Sumukh Bansal

Authors
  1. Aditya Tatu
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  2. Sumukh Bansal
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Editor information

Editors and Affiliations

  1. Department of Mathematics, University of Bergen, 7800, Bergen, Norway

    Xue-Cheng Tai

  2. Department of Mathematics, University of California, Los Angeles, CA, USA

    Egil Bae

  3. The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, S.A.R.

    Tony F. Chan

  4. Telemark University College, Postboks 203, 3901, Porsgrunn, Norway

    Marius Lysaker

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Tatu, A., Bansal, S. (2015). A Novel Active Contour Model for Texture Segmentation. In: Tai, XC., Bae, E., Chan, T.F., Lysaker, M. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2015. Lecture Notes in Computer Science, vol 8932. Springer, Cham. https://doi.org/10.1007/978-3-319-14612-6_17

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  • DOI: https://doi.org/10.1007/978-3-319-14612-6_17

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14611-9

  • Online ISBN: 978-3-319-14612-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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