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Nonlinear Approximation of Spatiotemporal Data Using Diffusion Wavelets

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Computer Analysis of Images and Patterns (CAIP 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4673))

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Abstract

We present a multiscale, graph-based approach to 3D image analysis using diffusion wavelet bases, which were presented in [1].

Diffusion wavelets allow to obtain orthonormal bases of L 2 functions on graphs. This permits the study of classical wavelet algorithms (such as compression and denoising of functions in , , via nonlinear approximation) in this setting.

In this paper, we describe how this could be used in structure-preserving compression of image sequences, modelled as a whole as a weighted graph, as a first step towards structural spatiotemporal wavelet segmentation. We further discuss the possibilities for using this abstract approach in computer vision tasks.

Supported by the Austrian Science Fund under the grant FWF-P18716-N13.

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Authors and Affiliations

  1. Austrian Research Centers GmbH, Video and Safety Technology, Tech Gate Vienna, Donau-City-Str.1, 1220 Vienna, Austria

    Marie Wild

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  1. Marie Wild
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Walter G. Kropatsch Martin Kampel Allan Hanbury

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© 2007 Springer-Verlag Berlin Heidelberg

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Wild, M. (2007). Nonlinear Approximation of Spatiotemporal Data Using Diffusion Wavelets. In: Kropatsch, W.G., Kampel, M., Hanbury, A. (eds) Computer Analysis of Images and Patterns. CAIP 2007. Lecture Notes in Computer Science, vol 4673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74272-2_110

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  • DOI: https://doi.org/10.1007/978-3-540-74272-2_110

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74271-5

  • Online ISBN: 978-3-540-74272-2

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