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Toward reliable experiments on the performance of Connected Components Labeling algorithms

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Abstract

The problem of labeling the connected components of a binary image is well defined, and several proposals have been presented in the past. Since an exact solution to the problem exists, algorithms mainly differ on their execution speed. In this paper, we propose and describe YACCLAB, Yet Another Connected Components Labeling Benchmark. Together with a rich and varied dataset, YACCLAB contains an open source platform to test new proposals and to compare them with publicly available competitors. Textual and graphical outputs are automatically generated for many kinds of tests, which analyze the methods from different perspectives. An extensive set of experiments among state-of-the-art techniques is reported and discussed.

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Notes

  1. 1.

    https://github.com/prittt/YACCLAB.

  2. 2.

    http://www.gutenberg.org.

  3. 3.

    After the appearance of the YACCLAB project results, OpenCV changed its default algorithm to the fastest one reported in YACCLAB.

  4. 4.

    SAUF and BBDT are the algorithms currently included in the OpenCV library (since version 3.2).

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Correspondence to Costantino Grana.

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Bolelli, F., Cancilla, M., Baraldi, L. et al. Toward reliable experiments on the performance of Connected Components Labeling algorithms. J Real-Time Image Proc (2018). https://doi.org/10.1007/s11554-018-0756-1

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Keywords

  • Connected Components Labeling
  • Benchmarking
  • Performance Evaluation