Gray-Scale Thinning Algorithm Using Local Min/Max Operations

  • Kyoung Min Kim
  • Buhm Lee
  • Nam Sup Choi
  • Gwan Hee Kang
  • Joong Jo Park
  • Ching Y. Suen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3872)

Abstract

A gray-scale thinning algorithm based on local min/max operations is newly proposed. Erosion and dilation properties of local min/max operations create new ridges from the given image. Thus grey scale skeletons can be effectively obtained by accumulating such ridges. The proposed method is quite salient because it can be also applied to an unsegmented image in which objects are not specified.

Keywords

Grayscale Image Grey Image Connectivity Problem Large Pixel Dilation Property 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Lam, L., Lee, S.W., Suen, C.Y.: Thinning Methodologies - A Comprehensive Survey. IEEE Trans. Pattern Analysis and Machine Intelligence 14, 869–885 (1992)CrossRefGoogle Scholar
  2. 2.
    Govindan, V.K., Shivaprasad, A.P.: A pattern adaptive thinning algorithm. Pattern Recognition 20, 623–637 (1987)CrossRefGoogle Scholar
  3. 3.
    Zhang, T.Y., Suen, C.Y.: A fast parallel algorithm for thinning digital patterns. Comm. of the ACM 27, 236–239 (1984)CrossRefGoogle Scholar
  4. 4.
    Peleg, S., Rosenfeld, A.: A min-max medial axis transformation. IEEE Trans. Pattern Analysis and Machine Intelligence 3, 208–210 (1981)CrossRefGoogle Scholar
  5. 5.
    Salari, E., Sly, P.: The Ridge-Seeking Method for Obtaining the Skeleton of Digital Images. IEEE Trans. System, Man, and Cybernetics 14, 524–528 (1984)Google Scholar
  6. 6.
    Wang, C., Abe, K.: ABE: A Method of Gray-scale Image Thinning: The Case without Region specification for Thinning. IEEE 11th Int. Conf. on Pattern Recognition 3, 404–407 (1992)Google Scholar
  7. 7.
    Nakagawa, Y., Rosenfeld, A.: A note on the use of local min and max operations in digital picture processing. IEEE Trans. System, Man, and Cybernetics 8, 632–635 (1978)MATHCrossRefGoogle Scholar
  8. 8.
    Kim, K.M., Park, J.J., Song, M.H., Kim, I.C., Suen, C.Y.: Detection of ridges and ravines using fuzzy logic operations. Pattern Recognition Letters 25, 743–751 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kyoung Min Kim
    • 1
    • 2
  • Buhm Lee
    • 2
  • Nam Sup Choi
    • 2
  • Gwan Hee Kang
    • 2
  • Joong Jo Park
    • 1
    • 3
  • Ching Y. Suen
    • 1
  1. 1.Centre for Pattern Recognition and Machine Intelligence (CENPARMI)Concordia UniversityMontrealCanada
  2. 2.Department of Electrical EngineeringYosu National UniversityJeollanam-doKorea
  3. 3.Department of Control and Instrumentation EngineeringGyeongsang National UniversityGyeongsangnam-doKorea

Personalised recommendations