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

  • Michael Backer
  • Jan Tünnermann
  • Bärbel Mertsching
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Image Segmentation Color Space Cluster Centroid Single Instruction Multiple Data Color Image Segmentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Michael Backer
    • 1
  • Jan Tünnermann
    • 1
  • Bärbel Mertsching
    • 1
  1. 1.GET LabUniversity of PaderbornPaderbornGermany

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