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  • 1.
    Jansson, Ylva
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Lindeberg, Tony
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields2017Report (Other academic)
    Abstract [en]

    This work presents a first evaluation of using spatiotemporal receptive fields from a recently proposed time-causal spatio-temporal scale-space framework as primitives for video analysis. We propose a new family of video descriptors based on regional statistics of spatio-temporal receptive field responses and evaluate this approach on the problem of dynamic texture recognition. Our approach generalises a previously used method, based on joint histograms of receptive field responses, from the spatial to the spatio-temporal domain and from object recognition to dynamic texture recognition. The time-recursive formulation enables computationally efficient time-causal recognition.

    The experimental evaluation demonstrates competitive performance compared to state-of-the-art. Especially, it is shown that binary versions of our dynamic texture descriptors achieve improved performance compared to a large range of similar methods using different primitives either handcrafted or learned from data. Further, our qualitative and quantitative investigation into parameter choices and the use of different sets of receptive fields highlights the robustness and flexibility of our approach. Together, these results support the descriptive power of this family of time-causal spatio-temporal receptive fields, validate our approach for dynamic texture recognition and point towards the possibility of designing a range of video analysis methods based on these new time-causal spatio-temporal primitives.

  • 2.
    Jansson, Ylva
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Lindeberg, Tony
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields2018In: Journal of Mathematical Imaging and Vision, ISSN 0924-9907, E-ISSN 1573-7683, Vol. 60, no 9, p. 1369-1398Article in journal (Refereed)
    Abstract [en]

    This work presents a first evaluation of using spatio-temporal receptive fields from a recently proposed time-causal spatiotemporal scale-space framework as primitives for video analysis. We propose a new family of video descriptors based on regional statistics of spatio-temporal receptive field responses and evaluate this approach on the problem of dynamic texture recognition. Our approach generalises a previously used method, based on joint histograms of receptive field responses, from the spatial to the spatio-temporal domain and from object recognition to dynamic texture recognition. The time-recursive formulation enables computationally efficient time-causal recognition. The experimental evaluation demonstrates competitive performance compared to state of the art. In particular, it is shown that binary versions of our dynamic texture descriptors achieve improved performance compared to a large range of similar methods using different primitives either handcrafted or learned from data. Further, our qualitative and quantitative investigation into parameter choices and the use of different sets of receptive fields highlights the robustness and flexibility of our approach. Together, these results support the descriptive power of this family of time-causal spatio-temporal receptive fields, validate our approach for dynamic texture recognition and point towards the possibility of designing a range of video analysis methods based on these new time-causal spatio-temporal primitives.

  • 3.
    Jansson, Ylva
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Lindeberg, Tony
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Dynamic texture recognition using time-causal spatio-temporal scale-space filters2017In: Scale Space and Variational Methods in Computer Vision, Springer, 2017, Vol. 10302, p. 16-28Conference paper (Refereed)
    Abstract [en]

    This work presents an evaluation of using time-causal scale-space filters as primitives for video analysis. For this purpose, we present a new family of video descriptors based on regional statistics of spatiotemporal scale-space filter responses and evaluate this approach on the problem of dynamic texture recognition. Our approach generalises a previously used method, based on joint histograms of receptive field responses, from the spatial to the spatio-temporal domain. We evaluate one member in this family, constituting a joint binary histogram, on two widely used dynamic texture databases. The experimental evaluation shows competitive performance compared to previous methods for dynamic texture recognition, especially on the more complex DynTex database. These results support the descriptive power of time-causal spatio-temporal scale-space filters as primitives for video analysis.

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