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Accelerated superpixel image segmentation with a parallelized DBSCAN algorithm

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Abstract

Segmentation of an image into superpixel clusters is a necessary part of many imaging pathways. In this article, we describe a new routine for superpixel image segmentation (F-DBSCAN) based on the DBSCAN algorithm that is six times faster than previous existing methods, while being competitive in terms of segmentation quality and resistance to noise. The gains in speed are achieved through efficient parallelization of the cluster search process by limiting the size of each cluster thus enabling the processes to operate in parallel without duplicating search areas. Calculations are performed in large consolidated memory buffers which eliminate fragmentation and maximize memory cache hits thus improving performance. When tested on the Berkeley Segmentation Dataset, the average processing speed is 175 frames/s with a Boundary Recall of 0.797 and an Achievable Segmentation Accuracy of 0.944.

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Authors and Affiliations

  1. Faculty of Medicine and Health Sciences, University of Auckland, Auckland, New Zealand

    Seng Cheong Loke

  2. Faculty of Engineering, University of Auckland, Auckland, New Zealand

    Bruce A. MacDonald

  3. University of Waikato, Hamilton, New Zealand

    Matthew Parsons

  4. Faculty of Science, University of Auckland, Auckland, New Zealand

    Burkhard Claus Wünsche

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  1. Seng Cheong Loke
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  2. Bruce A. MacDonald
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  3. Matthew Parsons
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  4. Burkhard Claus Wünsche
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Correspondence to Seng Cheong Loke.

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This study was paid for using PRESS account funding from the University of Auckland (ID 663710048). The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article.

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Loke, S.C., MacDonald, B.A., Parsons, M. et al. Accelerated superpixel image segmentation with a parallelized DBSCAN algorithm. J Real-Time Image Proc 18, 2361–2376 (2021). https://doi.org/10.1007/s11554-021-01128-5

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  • DOI: https://doi.org/10.1007/s11554-021-01128-5

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