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Variational Image Motion Computation: Theoretical Framework, Problems and Perspectives

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Mustererkennung 2000

Part of the book series: Informatik aktuell ((INFORMAT))

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Abstract

Many differential methods for the recovery of the optic flow field from an image sequence can be expressed in terms of a variational problem where the optic flow minimizes some energy. Typically, these energy functionals consist of two terms: a data term, which requires e.g. that a brightness constancy assumption holds, and a regularizer that encourages global or piecewise smoothness of the flow field. In this paper we present a systematic Classification of rotation invariant convex regularizers by exploring their connection to diffusion filters for multichannel images. This taxonomy provides a unifying framework for data-driven and flow-driven, isotropic and anisotropic, as well as spatial and spatiotemporal regularizers. While some of these techniques are classic methods from the literature, others are derived here for the first time. We conclude by pointing out some important research issues related to variational optic flow computation.

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Authors and Affiliations

  1. Computer Vision, Graphics, and Pattern Recognition Group, Dept. of Math, and Comp. Science, University of Mannheim, 68131, Mannheim, Germany

    Christoph Schnörr & Joachim Weickert

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  1. Christoph Schnörr
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  2. Joachim Weickert
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Editors and Affiliations

  1. Institut für Informatik und Praktische Mathematik, Christian-Albrechts Universität Kiel, Preußerstr. 1-9, 24105, Kiel, Deutschland

    Gerald Sommer , Norbert Krüger  & Christian Perwass ,  & 

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Schnörr, C., Weickert, J. (2000). Variational Image Motion Computation: Theoretical Framework, Problems and Perspectives. In: Sommer, G., Krüger, N., Perwass, C. (eds) Mustererkennung 2000. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59802-9_60

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  • DOI: https://doi.org/10.1007/978-3-642-59802-9_60

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67886-1

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