Pattern Analysis and Applications

, Volume 14, Issue 2, pp 149–163 | Cite as

Line segment detection using weighted mean shift procedures on a 2D slice sampling strategy

  • Marcos NietoEmail author
  • Carlos Cuevas
  • Luis Salgado
  • Narciso García
Theoretical Advances


A new line segment detection approach is introduced in this paper for its application in real-time computer vision systems. It has been designed to work unsupervised without any prior knowledge of the imaged scene; hence, it does not require tuning of input parameters. Although many works have been presented on this topic, as far as we know, none of them achieves a trade-off between accuracy and speed as our strategy does. The reduction of the computational cost compared to other fast methods is based on a very efficient sampling strategy that sequentially proposes points on the image that likely belong to line segments. Then, a fast line growing algorithm is applied based on the Bresenham algorithm, which is combined with a modified version of the mean shift algorithm to provide accurate line segments while being robust against noise. The performance of this strategy is tested for a wide variety of images, comparing its results with popular state-of-the-art line segment detection methods. The results show that our proposal outperforms these works considering simultaneously accuracy in the results and processing speed.


Line segment Eigenvalues Real time Slice sampling Mean shift Bresenham algorithm 



This work has been partially supported by the Ministerio de Ciencia e Innovación of the Spanish Government under project TEC2007-67764 (SmartVision), and by the Comunidad de Madrid under project S-0505/TIC-0223 (Pro-Multidis).


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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Marcos Nieto
    • 1
    Email author
  • Carlos Cuevas
    • 2
  • Luis Salgado
    • 2
  • Narciso García
    • 2
  1. 1.Vicomtech-ik4Donostia-San SebastiánSpain
  2. 2.Grupo de Tratamiento de ImágenesUniversidad Politécnica de MadridMadridSpain

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