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Continuous Curve Matching with Scale-Space Curvature and Extrema-Based Scale Selection

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Scale Space Methods in Computer Vision (Scale-Space 2003)

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Abstract

We extend a symmetric parametric curve matching algorithm designed for recognition and morphometry by incorporating Gaussian smoothing and curvature scale-space. A general statement of the matching theory and the properties of the associated algorithm is given. Gaussian smoothing is used to assist in approximating the continuous solution from the discrete solution given by dynamic programming. The method is then investigated in a multi-scale framework, which has the advantage of reducing the effects of noise and occlusion. A novel scale-space derived energy functional that incorporates geometric information from many scales at once is proposed. The related issue of selecting a smoothing kernel for a given matching problem is also explored, resulting in a topologically based method of scale-selection. This application requires estimating the matching between the fine and coarse scale versions of the same curve. We provide a tool for finding this inter-scale, intra-curve correspondence, based on tracking curvature extrema through scales. These novel algorithms are demonstrated on both 2D and 3D data.

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Author information

Authors and Affiliations

  1. University of Pennsylvania, Philadelphia, PA, USA, 19104-2644

    Brian Avants & James Gee

Authors
  1. Brian Avants
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  2. James Gee
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Editors and Affiliations

  1. Imaging Sciences, 5th Floor Thomas Guy House, Guy’s Campus, London, SE1 9RT, UK

    Lewis D. Griffin

  2. IT University of Copenhagen, Glentevej 67-69, Copenhagen NV, Denmark

    Martin Lillholm

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© 2003 Springer-Verlag Berlin Heidelberg

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Avants, B., Gee, J. (2003). Continuous Curve Matching with Scale-Space Curvature and Extrema-Based Scale Selection. In: Griffin, L.D., Lillholm, M. (eds) Scale Space Methods in Computer Vision. Scale-Space 2003. Lecture Notes in Computer Science, vol 2695. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44935-3_56

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  • DOI: https://doi.org/10.1007/3-540-44935-3_56

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40368-5

  • Online ISBN: 978-3-540-44935-5

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