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On the Choice of Tensor Estimation for Corner Detection, Optical Flow and Denoising

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Computer Vision - ACCV 2014 Workshops (ACCV 2014)

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Abstract

Many image processing methods such as corner detection, optical flow and iterative enhancement make use of image tensors. Generally, these tensors are estimated using the structure tensor. In this work we show that the gradient energy tensor can be used as an alternative to the structure tensor in several cases. We apply the gradient energy tensor to common image problem applications such as corner detection, optical flow and image enhancement. Our experimental results suggest that the gradient energy tensor enables real-time tensor-based image enhancement using the graphical processing unit (GPU) and we obtain 40 % increase of frame rate without loss of image quality.

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Acknowledgement

This research has received funding from the Swedish Research Council through grants for the projects Visualization-adaptive Iterative Denoising of Images (VIDI) and Extended Target Tracking (ETT), within the Linnaeus environment CADICS and the excellence network ELLIIT.

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Authors and Affiliations

  1. Computer Vision Laboratory, Linköping University, Linköping, Sweden

    Freddie Åström & Michael Felsberg

  2. Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden

    Freddie Åström & Michael Felsberg

Authors
  1. Freddie Åström
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  2. Michael Felsberg
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Corresponding author

Correspondence to Freddie Åström .

Editor information

Editors and Affiliations

  1. Center for Visual Information Technology, International Institute of Information Technology, Hyderabad, India

    C.V. Jawahar

  2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

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Åström, F., Felsberg, M. (2015). On the Choice of Tensor Estimation for Corner Detection, Optical Flow and Denoising. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9009. Springer, Cham. https://doi.org/10.1007/978-3-319-16631-5_2

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  • DOI: https://doi.org/10.1007/978-3-319-16631-5_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16630-8

  • Online ISBN: 978-3-319-16631-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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