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Image Descriptors Based on Curvature Histograms

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Pattern Recognition (GCPR 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8753))

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Abstract

Descriptors based on orientation histograms are widely used in computer vision. The spatial pooling involved in these representations provides important invariance properties, yet it is also responsible for the loss of important details. In this paper, we suggest a way to preserve the details described by the local curvature. We propose a descriptor that comprises the direction and magnitude of curvature and naturally expands classical orientation histograms like SIFT and HOG. We demonstrate the general benefit of the expansion exemplarily for image classification, object detection, and descriptor matching.

Supported by a scholarship of the Deutsche Telekom Stiftung.

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Acknowledgments

The work was partially funded by the ERC Starting Grant VideoLearn.

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Authors and Affiliations

  1. Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany

    Philipp Fischer & Thomas Brox

Authors
  1. Philipp Fischer
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  2. Thomas Brox
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Correspondence to Philipp Fischer .

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Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Münster, Münster, Germany

    Xiaoyi Jiang

  2. Computer Science Department 5, University of Erlangen-Nürnberg, Erlangen, Germany

    Joachim Hornegger

  3. Department of Computer Science, University of Kiel, Kiel, Germany

    Reinhard Koch

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Fischer, P., Brox, T. (2014). Image Descriptors Based on Curvature Histograms. In: Jiang, X., Hornegger, J., Koch, R. (eds) Pattern Recognition. GCPR 2014. Lecture Notes in Computer Science(), vol 8753. Springer, Cham. https://doi.org/10.1007/978-3-319-11752-2_19

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  • DOI: https://doi.org/10.1007/978-3-319-11752-2_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11751-5

  • Online ISBN: 978-3-319-11752-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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