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Evolutionary Approximation of Edge Detection Circuits

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Book cover Genetic Programming (EuroGP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9594))

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Abstract

Approximate computing exploits the fact that many applications are inherently error resilient which means that some errors in their outputs can safely be exchanged for improving other parameters such as energy consumption or operation frequency. A new method based on evolutionary computing is proposed in this paper which enables to approximate edge detection circuits. Rather than evolving approximate edge detectors from scratch, key components of existing edge detector are replaced by their approximate versions obtained using Cartesian Genetic Programming (CGP). Various approximate edge detectors are then composed and their quality is evaluated using a database of images. The paper reports interesting edge detectors showing a good tradeoff between the quality of edge detection and implementation cost.

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Acknowledgements

This work was supported by the Czech science foundation project GA16-17538S.

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Authors and Affiliations

  1. Faculty of Information Technology, IT4Innovations Centre of Excellence, Brno University of Technology, Božetěchova 2, 612 66, Brno, Czech Republic

    Petr Dvoracek & Lukas Sekanina

Authors
  1. Petr Dvoracek
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  2. Lukas Sekanina
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Corresponding author

Correspondence to Petr Dvoracek .

Editor information

Editors and Affiliations

  1. Dalhousie University, Halifax, Nova Scotia, Canada

    Malcolm I. Heywood

  2. University College Dublin, Dublin, Ireland

    James McDermott

  3. Universidade Nova de Lisboa, Lisboa, Portugal

    Mauro Castelli

  4. CIUSC Dept Informatics Engg, University of Coimbra, Coimbra, Portugal

    Ernesto Costa

  5. Edinburgh Napier University, Edinburgh, United Kingdom

    Kevin Sim

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Dvoracek, P., Sekanina, L. (2016). Evolutionary Approximation of Edge Detection Circuits. In: Heywood, M., McDermott, J., Castelli, M., Costa, E., Sim, K. (eds) Genetic Programming. EuroGP 2016. Lecture Notes in Computer Science(), vol 9594. Springer, Cham. https://doi.org/10.1007/978-3-319-30668-1_2

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  • DOI: https://doi.org/10.1007/978-3-319-30668-1_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30667-4

  • Online ISBN: 978-3-319-30668-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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