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

Michael Backer, Jan Tunnermann, Barbel Mertsching
GET Lab, University of Paderborn, Pohlweg 47-49, 33098 Paderborn, Germany
Lecture Notes in Computer Science Volume 7686, pp 108-120, 2013
@incollection{backer2013parallel,

   year={2013},

   isbn={978-3-642-35892-0},

   booktitle={Facing the Multicore-Challenge III},

   volume={7686},

   series={Lecture Notes in Computer Science},

   editor={Keller, Rainer and Kramer, David and Weiss, Jan-Philipp},

   doi={10.1007/978-3-642-35893-7_10},

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

   url={http://dx.doi.org/10.1007/978-3-642-35893-7_10},

   publisher={Springer Berlin Heidelberg},

   author={Backer, Michael and Tunnermann, Jan and Mertsching, Barbel},

   pages={108-120}

}

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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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