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International Workshop on Computational Topology in Image Context

CTIC 2019: Computational Topology in Image Context pp 137–150Cite as

A Persistence-Based Approach to Automatic Detection of Line Segments in Images

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11382))

Abstract

Edge detection algorithms usually produce a discrete set of edgels (edge pixels) in a given image on a fixed pixel grid. We consider the harder problem of detecting continuous straight line segments at subpixel resolution. The state-of-the art Line Segment Detection Algorithm (LSDA) outputs unordered line segments whose total number cannot be easily controlled. Another motivation to improve the LSDA is to avoid intersections and small angles between line segments, hence difficulties in higher level tasks such as segmentation or contour extraction.

The new Persistent Line Segment Detector (PLSD) outputs only non-intersecting line segments and ranks them by a strength, hence the user can choose a number of segments. The main novelty is an automatic selection of strongest segments along any straight line by using the persistence from Topological Data Analysis. The experiments on the Berkeley Segmentation Database of 500 real-life images show that the new algorithm outperforms the LSDA on the important measure of Boundary Recall.

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Acknowledgments

We thank all reviewers for helpful suggestions. This work was supported by the EPSRC grant “Application-driven Topological Data Analysis” (EP/R018472/1).

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Authors and Affiliations

  1. Department of Computer Science, University of Liverpool, Liverpool, L69 3BX, UK

    Vitaliy Kurlin & Grzegorz Muszynski

  2. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Grzegorz Muszynski

Authors
  1. Vitaliy Kurlin
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  2. Grzegorz Muszynski
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Corresponding author

Correspondence to Vitaliy Kurlin .

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Editors and Affiliations

  1. Universidad de Málaga, Málaga, Spain

    Rebeca Marfil

  2. Universidad de Málaga, Málaga, Spain

    Mariletty Calderón

  3. University of Seville, Sevilla, Spain

    Fernando Díaz del Río

  4. University of Seville, Sevilla, Spain

    Pedro Real

  5. Universidad de Málaga, Málaga, Spain

    Antonio Bandera

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Kurlin, V., Muszynski, G. (2019). A Persistence-Based Approach to Automatic Detection of Line Segments in Images. In: Marfil, R., Calderón, M., Díaz del Río, F., Real, P., Bandera, A. (eds) Computational Topology in Image Context. CTIC 2019. Lecture Notes in Computer Science(), vol 11382. Springer, Cham. https://doi.org/10.1007/978-3-030-10828-1_11

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  • DOI: https://doi.org/10.1007/978-3-030-10828-1_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-10827-4

  • Online ISBN: 978-3-030-10828-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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