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Spatial Configuration of Local Shape Features for Discriminative Object Detection

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Advances in Visual Computing (ISVC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5875))

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Abstract

This paper proposes a discriminative object class detection and recognition based on spatial configuration of local shape features. We show how simple, redundant edge based features overcome the problem of edge fragmentation while the efficient use of geometrically related feature pairs allows us to construct a robust object shape matcher, invariant to translation, scale and rotation. These prerequisites are used for weakly supervised learning of object models as well as object class detection. The object models employing pairwise combination of redundant shape features exhibit remarkably accurate localization of similar objects even in the presence of clutter and moderate view point changes which is further exploited for model building, object detection and recognition.

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Author information

Authors and Affiliations

  1. Industrial Research Institute for Automation and Measurements, Al. Jerozolimskie 202, 02-486, Warsaw, Poland

    Lech Szumilas

  2. Vienna University of Technology, Favoritenstraße 9/1832, A-1040, Wien, Austria

    Horst Wildenauer

Authors
  1. Lech Szumilas
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  2. Horst Wildenauer
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Graduate School of Science and Engineering, Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama-shi, 338-8570, Saitama, Japan

    Yoshinori Kuno

  6. Institute for Infocomm Research, 21 Heng Mui Keng Terrace, P.O. Box, 119613, Singapore

    Junxian Wang

  7. Department of Computer Science & Information Engineering, Tamkang University, Tamsui, Taipei, Taiwan, R.O.C.

    Jun-Xuan Wang

  8. Microsoft Research, Redmond, WA, USA

    Junxian Wang

  9. Department of Informatics, Univ. of Zurich, Winterthurerstr. 190, P.O. Box, 8057, Zurich, Switzerland

    Renato Pajarola

  10. Lawrence Livermore National Laboratory, 94550, Livermore, CA, USA

    Peter Lindstrom

  11. University of Applied Sciences Bonn-Rhein-Sieg, 53754, Sankt Augustin, Germany

    André Hinkenjann

  12. Humana Inc., 40202, Louisville, KY, USA

    Miguel L. Encarnação

  13. SCI Institute & School of Computing, University of Utah, 84112, Salt Lake City, UT, USA

    Cláudio T. Silva

  14. Desert Research Institute, 89512, Reno, NV, USA

    Daniel Coming

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© 2009 Springer-Verlag Berlin Heidelberg

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Szumilas, L., Wildenauer, H. (2009). Spatial Configuration of Local Shape Features for Discriminative Object Detection. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5875. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10331-5_3

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  • DOI: https://doi.org/10.1007/978-3-642-10331-5_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10330-8

  • Online ISBN: 978-3-642-10331-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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