CFP last date
20 May 2024
Reseach Article

Performance Comparison of ZS and GH Skeletonization Algorithms

by Ritika Luthra, Gulshan Goyal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 121 - Number 24
Year of Publication: 2015
Authors: Ritika Luthra, Gulshan Goyal
10.5120/21885-5138

Ritika Luthra, Gulshan Goyal . Performance Comparison of ZS and GH Skeletonization Algorithms. International Journal of Computer Applications. 121, 24 ( July 2015), 32-38. DOI=10.5120/21885-5138

@article{ 10.5120/21885-5138,
author = { Ritika Luthra, Gulshan Goyal },
title = { Performance Comparison of ZS and GH Skeletonization Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 121 },
number = { 24 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 32-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume121/number24/21885-5138/ },
doi = { 10.5120/21885-5138 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:09:18.888381+05:30
%A Ritika Luthra
%A Gulshan Goyal
%T Performance Comparison of ZS and GH Skeletonization Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 121
%N 24
%P 32-38
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Skeletonization is a crucial step in many digital image processing applications like medical imaging, pattern recognition, fingerprint classification etc. The skeleton expresses the structural connectivities of the main component of an object and is one pixel in width. Present paper covers the aspects of pixel deletion criteria in the skeletonization algorithms needed to preserve the connectivity, topology, sensitivity of the binary images. Performance of different skeletonization algorithms can be measured in terms of different parameters such as thinning rate, number of connected components, execution time etc. Present paper focuses on thinning rate, number of connected components, execution time on Zhang and Suen algorithm and Guo and Hall algorithm.

References
  1. Abu-Ain W., Bataineh B., Abu-Ain T. and OmarK., “Skeletonization Algorithm for Binary Images”, Fourth International Conference on Electrical Engineering and Informatics(ICEEI) Elsevier, Vol. 11, pp.704-709, 2013.
  2. Padole G.V. and Pokle S. B., “New Iterative Algorithms for Thinning Binary Images” IEEE Third International Conference on Emerging Trends in Engineering and Technology, Vol. 7, pp. 166-171, 2010.
  3. Lam L, Lee S.W. and Suen C.Y., “Thinning methodologies-A comprehensive survey”, IEEE transactions on pattern analysis and machine intelligence, Vol. 14, No. 9, pp. 869-885, 1992.
  4. Chatbri H. and Kameyama K., “Using Scale Space Filtering to Make Thinning Algorithms Robust Against Noise in Sketch Images”, International Conference on Pattern Recognition letters Elsevier, Vol. 42, pp. 1-10, 2014.
  5. Zhang T.Y. and Suen C.Y., “A Fast Parallel Algorithm for Thinning Digital Patterns”, Communications of the Association of Computer Machinery (ACM), Vol. 27, No. 3, pp. 236-239, 1984.
  6. Zhou R.W., Quek C. and Ng G.S., “A Novel Single-Pass Thinning Algorithm and an Effective Set of Performance Criteria”, International Journal of Pattern Recognition Letters Elsevier, Vol. 16, Issue 12, pp. 1267-1275, 1995.
  7. Ahmed M. and Ward R., “A Rotation Invariant Rule-Based Thinning Algorithm for Character Recognition”, IEEE Journal on Pattern Analysis and Machine Intelligence, Vol. 24, No. 12, pp. 1672-1678, 2002.
  8. Rockett P.I., “An Improved Rotation-Invariant Thinning Algorithm”, IEEE Journal on Pattern Analysis and Machine Intelligence, Vol. 27, No. 10, pp.1671-1674, 2005.
  9. Huang L., Wan G. and Liu C., “An Improved Parallel Thinning Algorithm”, IEEE Seventh International Conference on Document Analysis and Recognition, Vol. 10, pp. 780-783, 2003.
  10. Saeed K., Tabedzki M., Rybnik M. and Adamski M., “K3M: A Universal Algorithm for Image Skeletonization and A Review of Thinning Techniques”, International Journal of Applied Mathematics & Computer Science, Vol. 20, No. 2, pp. 317–335, 2010
  11. Jagna A. and Kamakshiprasad V., “New parallel binary image thinning algorithm” ARPN Journal of Engineering and Applied sciences, Vol. 5, No. 4, pp. 64-67, 2010.
  12. Choudhary A., Rishi R. and Ahlawat S., “Off-Line Handwritten Character Recognition using Features Extracted from Binarization Technique”, American Applied Science Research Institute (AASRI) Conference on Intelligent Systems and Control, Vol. 4, pp. 306-312, 2013.
  13. Tarabek P., “Performance Measurements of Thinning Algorithms”, Journal of Information, Control and Management Systems, Vol. 6, No.2, pp. 125-132, 2008.
  14. Lin X. et al. “ A proof of image Euler number formula” Springer June 2006, Volume 49, Issue 3, pp 364-371
  15. Guo Z. and Hall R.W., “Parallel Thinning with Two-Sub Iteration Algorithms”, Communications of the Association of Computer Machinery (ACM) Image Processing and Computer Vision, Vol. 32, No. 3, pp. 359-373, 1989.
  16. Kwon J., “Improved Parallel Thinning Algorithm to Obtain Unit -Width Skeleton”, The International Journal of Multimedia & Its Applications (IJMA), Vol.5, No.2, pp. 1-14, 2013.
Index Terms

Computer Science
Information Sciences

Keywords

Skeletonization Optical character Recognition ZS GH ZSM