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Journals / Istanbul University Journal of Electrical and Electronics Engineering / 2006 / Cilt: 6 - Sayı: 2

A comparative study on entropic thresholding methods

İbrahim TÜRKOĞLU
Abdulkadir ŞENGÜR
M. Cevdet İNCE
Pages
183–188
Publication Date
2022-10-18
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

We perform experiments for demonstrating the effectiveness of the examined methods on image thresholding.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Image thresholding is an important task both for digital image processing applications and for pattern recognition.
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Key Findings & Data:Image segmentation by thresholding is the simplest technique.
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Conclusion & Impact:We perform experiments for demonstrating the effectiveness of the examined methods on image thresholding.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Image thresholding is an important task both for digital image processing applications and for pattern recognition. Image segmentation by thresholding is the simplest technique. In this study, we intent to carry out a comparative study of entropic thresholding methods. We examine several entropic thresholding methods which are the most popular in the literature. These methods are minimum cross entropy, maximum entropy sum method, Renyi's entropy and Havrda&Charvat entropy. We perform experiments for demonstrating the effectiveness of the examined methods on image thresholding.

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Cite This Article

TÜRKOĞLU, İ., ŞENGÜR, A., İNCE, M. C. (2022). A comparative study on entropic thresholding methods. Istanbul University Journal of Electrical and Electronics Engineering, Cilt: 6 - Sayı: 2, 183–188. https://ajindex.com/articles/543816

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References (15)

  1. Shannon C.E., A mathematical theory of communication, Bell Syst. Tech. J. vol. 27, pp. 379-423, July, 1948
  2. Pun T., Entropic thresholding, a new method, Computer Graphics Image Process. CGIP 16, 210-239, 1981
  3. Kapur J.N., Sahoo P.K. and Wong A.K.C., A new method of gray level picture thresholding using the entropy of the histogram. Comput. Vision Graphics Image Process.,29, 273-285, 1985
  4. Li E. H., Lee C. K., Minimum cross-entropy thresholding, Pattern Recognition, 617-625, 1992
  5. Pal N. R., On minimum cross-entropy thresholding, Pattern Recognition, vol. 29, pp. 575-580, 1996
  6. Brink A. D. and Pendock N. E., Minimum cross-entropy threshold selection, Pattern Recognition, vol. 29, no. 1, pp. 179-188, 1996
  7. Abutaleb A. S., Automatic thresholding of grey level pictures using two dimensional entropy, Comput. Vision Graphics Image process. Vol. 47, pp. 22-32, 1989
  8. Pal N. R. and Pal S. K., Entropy thresholding, Signal Process. Vol. 16, pp. 97-108, 1989
  9. Brink A. D., Thresholding of digital images using two dimensional entropies, Patter Recognition, vol. 25, no. 8, pp. 803-808, 1992
  10. Sahoo P. K. and Arora G., A thresholding method based on two dimensional Renyi’s entropy, Pattern Recognition, vol. 37, pp. 1149-1161, 2004
  11. İbrahim Türkoğlu, Durağan olmayan işaretler için zaman-frekans entropilerine dayalı akıllı örüntü tanıma, F.Ü.Fen Bilimleri Enstitüsü, Doktora Tezi,2002
  12. İbrahim Türkoğlu, Ahmet Arslan, Erdoğan İlkay, An expert system for diagnose of the heart valve diseases, Expert systems with applications, 23(3), 229-236, 2002
  13. Sezgin, M. and Sankur, B., Survey over image thresholding techniques and quantative performance evaluation, Journal of Electronic Imaging 13 (1), 146-165, January, 2004
  14. Renyi A., On measures of entropy and informations, Proc. Fourth Berkeley Symp. Math. Statist. Prob. pp. 547-56., 1960
  15. Havdra J. and Charvat F., Quantification method of classification processes: concept of structural alfa-entropy. Kybernetica, 3, (Prague) pp. 30-35, 1967.