한국멀티미디어학회논문지 (Journal of Korea Multimedia Society)

  • 제20권2호
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  • Pages.163-169
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  • 2017
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  • 1229-7771(pISSN)
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  • 2384-0102(eISSN)
한국멀티미디어학회 (Korea Multimedia Society)
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근사 임계값 추정을 통한 Otsu 알고리즘의 연산량 개선

A Computational Improvement of Otsu's Algorithm by Estimating Approximate Threshold

  • Lee, Youngwoo (Graduate School of Electronic & Computer Eng., Seokyeong University) ;
  • Kim, Jin Heon (Graduate School of Electronic & Computer Eng., Seokyeong University)
  • 투고 : 2017.01.13
  • 심사 : 2017.01.19
  • 발행 : 2017.02.28
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초록

There are various algorithms evaluating a threshold for image segmentation. Among them, Otsu's algorithm sets a threshold based on the histogram. It finds the between-class variance for all over gray levels and then sets the largest one as Otsu's optimal threshold, so we can see that Otsu's algorithm requires a lot of the computation. In this paper, we improved the amount of computational needs by using estimated Otsu's threshold rather than computing for all the threshold candidates. The proposed algorithm is compared with the original one in computation amount and accuracy. we confirm that the proposed algorithm is about 29 times faster than conventional method on single processor and about 4 times faster than on parallel processing architecture machine.

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참고문헌

  1. B. Sankur, and M. Sezgin, "Survey over Image Thresholding Techniques and Quantitative and Quantitative Performance Evaluation," Journal of Electronic Imaging, Vol. 13, No. 1, pp. 146-165, 2004. https://doi.org/10.1117/1.1631315
  2. A. Rosenfeld, and P. De la Torre, "Histogram Concavity Analysis as an Aid in Threshold Selection," IEEE Transactions on Systems Man Cybernetics, Vol. SMC-13, Issue 2, pp. 231-235, 1983. https://doi.org/10.1109/TSMC.1983.6313118
  3. M.I. Sezan, "A Peak Detection Algorithm and Its Application to Histogram-Based Image Data Reduction," Journal of Computer Vision, Graphics, and Image Processing, Vol. 49, No. 1, pp. 36-51, 1990. https://doi.org/10.1016/0734-189X(90)90161-N
  4. T.W. Riddler, and S. Calvard, "Picture Thre- Sholding Using an Iterative Selection Method," IEEE Transactions on Systems Man Cybernetics, Vol. SMC-8, No. 8, pp. 630-632, 1978.
  5. N. Otsu, "A Threshold Selection Method from Gray Level Histograms," IEEE Transactions on Systems Man Cybernetics, Vol. SMC-9, No. 1, pp. 62-66, 1979.
  6. D.E. Lloyd, Automatic Target Classification Using Moment Invariant of Image Shapes, Technical Report, RAE IDN AW126, Farnborough, UK, 1985.
  7. J.N. Kapur, P.K. Sahoo, and A.K.C. Wong, "A New Method for Gray-Level Picture Thre- Sholding Using the Entropy of the Histogram," Graph Models Image Processing, Vol. 29, Issue 3, pp. 273-285, 1985. https://doi.org/10.1016/0734-189X(85)90125-2
  8. H. Li, and P.K.S. Tam, "An Iterative Algorithm for Minimum Cross-Entropy Thresholding," Pattern Recognition Letters, Vol. 19, Issue 8, pp. 771-776, 1998. https://doi.org/10.1016/S0167-8655(98)00057-9
  9. W.H. Tsai, "Moment-Preserving Thresholding: A New Approach," Graph Models Image Processing, Vol. 19, pp. 377-393, 1985.
  10. L. Hertz, and R.W. Schafer, "Multilevel Thre- Solding Using Edge Matching," Computer Vision, Graphics, and Image Processing, Vol. 44, Issue 3, pp. 279-295, 1988. https://doi.org/10.1016/0734-189X(88)90125-9
  11. J.C. Russ, "Atomatic Discrimination of Features in Gray-Scale Images," Journal of Microscopy, Vol. 148, No. 3, pp. 263-277, 1987. https://doi.org/10.1111/j.1365-2818.1987.tb02872.x
  12. B. Chanda, and D.D. Majumder, "A Note on the Use of Gray Level Co-Occurrence Matrix in Threshold Selection," Signal Processing, Vol. 15, Issue 2, pp. 149-167, 1988. https://doi.org/10.1016/0165-1684(88)90067-9
  13. N. Friel, and I.S. Molchanov, "A New Thresholding Technique Based on Random Sets," Pattern Recognition, Vol. 32, Issue 9, 1999.
  14. W. Niblack, An Introduction to Image Processing, Prentice-Hall, Englewood Cliffs, pp. 115-116, 1986.
  15. J.M. White, and G.D. Rohrer, "Image Thresholding for Optical Character Recognition and Other Applications Requiring Character Image Extraction," IBM Journal of Research Development, Vol. 27, No. 4, pp. 400-411, 1983. https://doi.org/10.1147/rd.274.0400
  16. J.H. Kim, and G. Kim, "A Binarization Technique Using Histogram Matching for License Plate with a Shadow," Journal of Broadcast Engineering, Vol. 19, No. 1, pp. 56-63, 2014. https://doi.org/10.5909/JBE.2014.19.1.56
  17. H.J. Lee, and J.H. Chung, "Face Recognition Robust to Brightness, Contrast, Scale, Rotation and Translation," Journal of the Institute of Electronics Engineers of Korea, Vol. 40, No. 6, pp. 149-156, 2003.
  18. K.S. Kim, S.W. Shin, S. Lee, J.S. Jeong, W. Park, and K.D. Kim, "Color Image Segmentation for Extracting Dental Plaque," The Transactions of the Korean Institute of Electrical Engineers, Vol. 60, No. 6, pp. 1183-1189, 2011. https://doi.org/10.5370/KIEE.2011.60.6.1183
  19. G.H. Jang, H.H. Park, S.L. Lee, D.H. Kim, and M.K. Lim "An Effective Extraction Algorithm of Pulmonary Regions Using Intensity-Level Maps in Chest X-Ray Images," Journal of Korea Multimedia Society, Vol. 13, No. 7, pp. 1062-1075, 2010.
  20. J.M. Sung, H.G. Ha, and B.Y. Choi, "Image Thresholding Based on Within-Class Standard Deviation," Journal of the Institute of Electronics Engineers of Korea, Vol. 50, No. 7, pp. 1844-1852, 2013.
  21. Z.X. Li, and S.W. Kim, "A Multi-Thresholding Approach Improved with Otsu's Method," Journal of the Institute of Electronics Engineers of Korea, Vol. 43, No. 5, pp. 407-415, 2006.
  22. B.M. Singh, R. Sharma, A. Mittal,, and D. Ghosh, "Parallel Implementation of Otsu's Binarization Approach on Graphics Processing Unit," International Journal of Computer Applications, Vol. 32, No. 2, pp. 16-21, 2010.