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Bio-inspired Motion Estimation with Event-Driven Sensors

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Advances in Computational Intelligence (IWANN 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9094))

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Abstract

This paper presents a method for image motion estimation for event-based sensors. Accurate and fast image flow estimation still challenges Computer Vision. A new paradigm based on asynchronous event-based data provides an interesting alternative and has shown to provide good estimation at high contrast contours by estimating motion based on very accurate timing. However, these techniques still fail in regions of high-frequency texture. This work presents a simple method for locating those regions, and a novel phase-based method for event sensors that estimates more accurately these regions. Finally, we evaluate and compare our results with other state-of-the-art techniques.

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Author information

Authors and Affiliations

  1. University of Maryland, UMIACS, CFAR, A.V. Williams Bldg, College Park, MD, USA

    Francisco Barranco, Cornelia Fermuller & Yiannis Aloimonos

  2. University of Granada, CITIC, ETSIIT, Daniel Saucedo Aranda sn, Granada, Spain

    Francisco Barranco

Authors
  1. Francisco Barranco
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  2. Cornelia Fermuller
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  3. Yiannis Aloimonos
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Corresponding author

Correspondence to Francisco Barranco .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Malaga, Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Vilanova i la Geltrú, Spain

    Andreu Catala

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© 2015 Springer International Publishing Switzerland

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Barranco, F., Fermuller, C., Aloimonos, Y. (2015). Bio-inspired Motion Estimation with Event-Driven Sensors. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science(), vol 9094. Springer, Cham. https://doi.org/10.1007/978-3-319-19258-1_27

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  • DOI: https://doi.org/10.1007/978-3-319-19258-1_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19257-4

  • Online ISBN: 978-3-319-19258-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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