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Characterization and recognition of dynamic textures based on the 2D+T curvelet transform

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Abstract

The research context of this article is the recognition and description of dynamic textures. In image processing, the wavelet transform has been successfully used for characterizing static textures. To our best knowledge, only two works are using spatio-temporal multiscale decomposition based on the tensor product for dynamic texture recognition. One contribution of this article is to analyze and compare the ability of the 2D+T curvelet transform, a geometric multiscale decomposition, for characterizing dynamic textures in image sequences. Two approaches using the 2D+T curvelet transform are presented and compared using three new large databases. A second contribution is the construction of these three publicly available benchmarks of increasing complexity. Existing benchmarks are either too small not available or not always constructed using a reference database. Feature vectors used for recognition are described as well as their relevance, and performances of the different methods are discussed. Finally, future prospects are exposed.

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Notes

  1. 64-bit processor, 3.2GHz with 26Go Ram.

  2. http://projects.cwi.nl/dyntex/.

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Authors and Affiliations

  1. Laboratoire Hubert Curien, CNRS, UMR5516, 42000 , Saint-Étienne, France

    Sloven Dubois

  2. Laboratoire Mathématiques Image et Applications, Avenue Michel Crépeau, 17042 , La Rochelle, France

    Renaud Péteri

  3. Laboratoire Informatique, Image et Interaction, Avenue Michel Crépeau, 17042 , La Rochelle, France

    Michel Ménard

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  1. Sloven Dubois
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  2. Renaud Péteri
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  3. Michel Ménard
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Correspondence to Renaud Péteri.

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Dubois, S., Péteri, R. & Ménard, M. Characterization and recognition of dynamic textures based on the 2D+T curvelet transform. SIViP 9, 819–830 (2015). https://doi.org/10.1007/s11760-013-0532-4

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  • DOI: https://doi.org/10.1007/s11760-013-0532-4

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