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Contextual and Non-combinatorial Approach to Feature Extraction

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Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2683))

Abstract

Extracting features from an image is the first step in many computer vision applications. Traditionally, features represent physical or visual primitives such as edges and corners. In this paper, we augment the definition to include any attributes that conveniently describe the correlation and contextual relation between the primitive and its neighbors. The augmentation allows us to design a more detailed probability distribution model. If the distribution model is differentiable with respect to each attribute of a feature, a simple local search will find a feature set that is a local maximum in the joint probability distribution. Therefore, the final representation is free from noise and aliasing that perturbs the representation away from the local maximum. We can apply the approach to many low level vision tasks. In this paper, we demonstrate our approach with sub-pixel contour representation and surface reconstruction problems.

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Author information

Authors and Affiliations

  1. Computer Science and Engineering, University of South Carolina, Columbia, SC, 29208, USA

    Toshiro Kubota

Authors
  1. Toshiro Kubota
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Editor information

Editors and Affiliations

  1. Department of CISE, University of Florida, Gainesville, FL, USA

    Anand Rangarajan

  2. Instituto de Telecomunicações, Instituto Superior Técnico, T.U. Lisbon, Av. Rovisco Pais, 1049-001, Lisboa, Portugal

    Mário Figueiredo

  3. ARIANA Research Group, INRIA/I3S, Sophia Antipolis, France

    Josiane Zerubia

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© 2003 Springer-Verlag Berlin Heidelberg

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Kubota, T. (2003). Contextual and Non-combinatorial Approach to Feature Extraction. In: Rangarajan, A., Figueiredo, M., Zerubia, J. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2003. Lecture Notes in Computer Science, vol 2683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45063-4_30

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  • DOI: https://doi.org/10.1007/978-3-540-45063-4_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40498-9

  • Online ISBN: 978-3-540-45063-4

  • eBook Packages: Springer Book Archive

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