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International Conference on Neural Information Processing

ICONIP 2006: Neural Information Processing pp 334–342Cite as

A Novel Split-and-Merge Technique for Error-Bounded Polygonal Approximation

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4233))

Abstract

How to use a polygon with the fewest possible sides to approximate a shape boundary is an important issue in pattern recognition and image processing. A novel split-and-merge technique(SMT) is proposed. SMT starts with an initial shape boundary segmentation, split and merge are then alternately done against the shape boundary. The procedure is halted when the pre-specified iteration number is achieved. For increasing stability of SMT and improving its robustness to the initial segmentation, a ranking-selection scheme is utilized to choose the splitting and merging points. The experimental results show its superiority.

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References

  1. Rafael, C.G., Richard, E.W.: Digital Image Processing, pp. 648–649. Prentice-Hall, Englewood Cliffs (2002)

    Google Scholar 

  2. Sklansky, J., Gonzalez, V.: Fast Polygonal Approximation of Digitized Curves. Pattern Recognition 12, 327–331 (1980)

    Article  Google Scholar 

  3. Ray, B.K., Ray, K.S.: Determination of Optimal Polygon from Digital Curve Using L 1 Norm. Pattern Recognition 26, 505–509 (1993)

    Article  Google Scholar 

  4. Ramer, U.: An Iterative Procedure for the Polygonal Approximation of Plane Curves. Comput. Graph. Image. Process. 1, 244–256 (1972)

    Article  Google Scholar 

  5. Pikaz, A., Dinstein, I.: An Algorithm for Polygonal Approximation Based in Iterative Point Elimination. Pattern Recognition Letters 16(6), 557–563 (1995)

    Article  Google Scholar 

  6. Yin, P.Y.: Genetic Algorithms for Polygonal Approximation of Digital Curves. Int. J. Pattern Recognition Artif. Intell. 13, 1–22 (1999)

    Article  Google Scholar 

  7. Goldberge, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)

    Google Scholar 

  8. Baker, J.F.: Adaptive Selection Methods for Genetic Algorithms. In: Grefenstette, J.J. (ed.) Proc. of the 1st Int’1. Conf. on Genetic Algorithms, pp. 101–111. Lawrence Earlbaum Associates, Hilladale, NJ (1985)

    Google Scholar 

  9. Teh, H.C., Chin, R.T.: On Detection of Dominant Points on Digital Curves. IEEE Trans. Pattern Anal. Mach. Intell. 11(8), 859–872 (1989)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Fudan University, Shanghai, 200433, P.R. China

    Bin Wang & Chaojian Shi

  2. Merchant Marine College, Shanghai Maritime University, Shanghai, 200135, P.R. China

    Chaojian Shi

Authors
  1. Bin Wang
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  2. Chaojian Shi
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Engineering, The Chinese Univ. of Hong Kong, Shatin, N.T., Hong Kong

    Irwin King

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  3. The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Lai-Wan Chan

  4. Department of Computer Science and Engineering & Center for Cognitive Science, The Ohio State University, OH 43210, Columbus

    DeLiang Wang

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© 2006 Springer-Verlag Berlin Heidelberg

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Wang, B., Shi, C. (2006). A Novel Split-and-Merge Technique for Error-Bounded Polygonal Approximation. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_37

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  • DOI: https://doi.org/10.1007/11893257_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46481-5

  • Online ISBN: 978-3-540-46482-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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