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Facing the Multicore-Challenge III pp 108–120Cite as

Parallel k-Means Image Segmentation Using Sort, Scan and Connected Components on a GPU

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7686))

Abstract

Image segmentation is required to run fast and without supervision to speed up subsequent processes such as object recognition or other high level tasks. General purpose computing on the GPU is a powerful tool to perform efficient image processing and has been applied to the image segmentation problem. However, state-of-the-art approaches still perform parts of the computations on the CPU requiring costly data exchange with the main memory. In this paper we suggest a fully unsupervised color image segmentation that runs completely on the GPU including the calculation of region features. We compare our results to a popular CPU-based and a recent GPU-based method and report a computation time advantage.

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Author information

Authors and Affiliations

  1. GET Lab, University of Paderborn, Pohlweg 47-49, 33098, Paderborn, Germany

    Michael Backer, Jan Tünnermann & Bärbel Mertsching

Authors
  1. Michael Backer
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  2. Jan Tünnermann
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  3. Bärbel Mertsching
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Editor information

Editors and Affiliations

  1. Department of Topology, Computer Science and Mathematics, University of Applied Science, Schellingstraße 24, 70174, Stuttgart, Germany

    Rainer Keller

  2. Institute of Computer Science and Engineering, Karlsruhe Institute of Technology (KIT), Haid-und-Neu-Straße 7, 76131, Karlsruhe, Germany

    David Kramer

  3. Institute for Applied and Numerical Mathematics 4, Karlsruhe Institut of Technology, Fritz-Erler-Straße 23, 76133, Karlsruhe, Germany

    Jan-Philipp Weiss

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Backer, M., Tünnermann, J., Mertsching, B. (2013). Parallel k-Means Image Segmentation Using Sort, Scan and Connected Components on a GPU. In: Keller, R., Kramer, D., Weiss, JP. (eds) Facing the Multicore-Challenge III. Lecture Notes in Computer Science, vol 7686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35893-7_10

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  • DOI: https://doi.org/10.1007/978-3-642-35893-7_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35892-0

  • Online ISBN: 978-3-642-35893-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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