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An Effective Principal Curves Extraction Algorithm for Complex Distribution Dataset

  • Hongyun Zhang
  • Duoqian Miao
  • Lijun Sun
  • Ying Ye
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6401)

Abstract

This paper proposes a new method for finding principal curves from complex distribution dataset. Motivated by solving the problem, which is that existing methods did not perform well on finding principal curve in complex distribution dataset with high curvature, high dispersion and self-intersecting, such as spiral-shaped curves, Firstly, rudimentary principal graph of data set is created based on the thinning algorithm, and then the contiguous vertices are merged. Finally the fitting-and-smoothing step introduced by Kégl is improved to optimize the principal graph, and Kégl’s restructuring step is used to rectify imperfections of principal graph. Experimental results indicate the effectiveness of the proposed method on finding principal curves in complex distribution dataset.

Keywords

Principal curves Complex distribution dataset Thinning algorithm Fitting-smoothing step Image skeletonization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Hongyun Zhang
    • 1
    • 2
  • Duoqian Miao
    • 1
    • 2
  • Lijun Sun
    • 1
    • 2
  • Ying Ye
    • 1
    • 2
  1. 1.The Key Laboratory of Embedded System and Service Computing, Ministry of EducationChina, Tongji UniversityShanghaiChina
  2. 2.School of Electronic and Information EngineeringTongji UniversityShanghaiChina

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