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Deep Learning vs. Traditional Computer Vision

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Advances in Computer Vision (CVC 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 943))

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Abstract

Deep Learning has pushed the limits of what was possible in the domain of Digital Image Processing. However, that is not to say that the traditional computer vision techniques which had been undergoing progressive development in years prior to the rise of DL have become obsolete. This paper will analyse the benefits and drawbacks of each approach. The aim of this paper is to promote a discussion on whether knowledge of classical computer vision techniques should be maintained. The paper will also explore how the two sides of computer vision can be combined. Several recent hybrid methodologies are reviewed which have demonstrated the ability to improve computer vision performance and to tackle problems not suited to Deep Learning. For example, combining traditional computer vision techniques with Deep Learning has been popular in emerging domains such as Panoramic Vision and 3D vision for which Deep Learning models have not yet been fully optimised.

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Authors and Affiliations

  1. IMaR Technology Gateway, Institute of Technology Tralee, Tralee, Ireland

    Niall O’Mahony, Sean Campbell, Anderson Carvalho, Suman Harapanahalli, Gustavo Velasco Hernandez, Lenka Krpalkova, Daniel Riordan & Joseph Walsh

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  1. Niall O’Mahony
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  2. Sean Campbell
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  3. Anderson Carvalho
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  4. Suman Harapanahalli
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  5. Gustavo Velasco Hernandez
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  6. Lenka Krpalkova
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  7. Daniel Riordan
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  8. Joseph Walsh
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Correspondence to Niall O’Mahony .

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Editors and Affiliations

  1. Saga University, Saga, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

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O’Mahony, N. et al. (2020). Deep Learning vs. Traditional Computer Vision. In: Arai, K., Kapoor, S. (eds) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-17795-9_10

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