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RANVEC and the Arc Segmentation Contest

  • Xavier Hilaire
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2390)

Abstract

This paper briefly describes an experimental arc extraction algorithm that ran the Arc Segmentation Contest at GREC’2001. As the proposed method is based on the one detailed in [5], this paper only describes the improvments we brought to the original method. We first review some rules from the evaluation protocol that helped us to make major assumptions while designing the algorithm. We then explain the method, and discuss the results we obtained in various cases. Finally, we give some conclusions and introduce a possible extension to this method.

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References

  1. 1.
    G. Sanniti di Baja. Well-Shaped, Stable, and Reversible Skeletons from the (3,4)-Distance Transform. Journal of Visual Communication and Image Representation, 5(1):107–115, 1994.CrossRefGoogle Scholar
  2. 2.
    K. Tombre, Ch. Ah-Soon, Ph. Dosch, G. Masini, S. Tabbone. Stable and Robust Vectorization: How to Make the Right Choices. In A.K. Chhabra and D. Dori, editors, Graphics Recognition-Recent Advances, volume 1941 of Lecture Notes in Computer Science, pages 3–18. Springer Verlag, 2000.CrossRefGoogle Scholar
  3. 3.
  4. 4.
    Liu Wen-Yin and Dov Dori. A Protocol for Performance Evaluation of Line Detection Algorithms. Machine Vision and Applications, Special Issue on Performance Characteristics of Vision Algorithms, 9(5/6):240–250, 1997.Google Scholar
  5. 5.
    Xavier HILAIRE and Karl TOMBRE. Inproving the Accuracy of Skeleton-Based Vectorization. In Proceedings of the Fourth IAPR International Workshop on Graphics Recognition, pages 381–394, 2001.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Xavier Hilaire
    • 1
    • 2
  1. 1.LORIAVandœuvre-lés-NancyFrance
  2. 2.FS2iVersaillesFrance

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