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A Biologically Motivated Scheme for Robust Junction Detection

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Biologically Motivated Computer Vision (BMCV 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2525))

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Abstract

Junctions provide important cues in various perceptual tasks, such as the determination of occlusion relationship for figureground separation, transparency perception, and object recognition, among others. In computer vision, junctions are used in a number of tasks like point matching for image tracking or correspondence analysis. We propose a biologically motivated approach to junction detection. The core component is a model of V1 based on biological mechanisms of colinear long-range integration and recurrent interaction. The model V1 interactions generate a robust, coherent representation of contours. Junctions are then implicitly characterized by high activity for multiple orientations within a cortical hypercolumn. A local measure of circular variance is used to extract junction points from this distributed representation. We show for a number of generic junction configurations and various artificial and natural images that junctions can be accurately and robustly detected. In a first set of simulations, we compare the detected junctions based on recurrent long-range responses to junction responses as obtained for a purely feedforward model of complex cells. We show that localization accuracy and positive correctness is improved by recurrent long-range interaction. In a second set of simulations, we compare the new scheme with two widely used junction detection schemes in computer vision, based on Gaussian curvature and the structure tensor. Receiver operator characteristic (ROC) analysis is used for a threshold-free evaluation of the different approaches. We show for both artificial and natural images that the new approach performs superior to the standard schemes. Overall we propose that nonlocal interactions as realized by long-range interactions within V1 play an important role for the detection of higher order features such as corners and junctions.

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Authors and Affiliations

  1. Dept. of Neural Information Processing, Univ. Ulm, D-89069, Ulm, Germany

    Thorsten Hansen & Heiko Neumann

Authors
  1. Thorsten Hansen
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  2. Heiko Neumann
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Editor information

Editors and Affiliations

  1. Max Planck Institute for Biological Cybernetics, Spemannstraße 38, 72076, Tübingen, Germany

    Heinrich H. Bülthoff  & Christian Wallraven  & 

  2. Department of Computer Science and Engineering, Korea University, Anam-dong, Seongbuk-ku, 136-701, Seoul, Korea

    Seong-Whan Lee

  3. Department of Brain and Cognitive Sciences, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 45 Carleton Street, 02142, Cambridge, MA, USA

    Tomaso A. Poggio

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© 2002 Springer-Verlag Berlin Heidelberg

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Hansen, T., Neumann, H. (2002). A Biologically Motivated Scheme for Robust Junction Detection. In: Bülthoff, H.H., Wallraven, C., Lee, SW., Poggio, T.A. (eds) Biologically Motivated Computer Vision. BMCV 2002. Lecture Notes in Computer Science, vol 2525. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36181-2_2

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  • DOI: https://doi.org/10.1007/3-540-36181-2_2

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00174-4

  • Online ISBN: 978-3-540-36181-7

  • eBook Packages: Springer Book Archive

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