Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Threshold Selection Method in Gray Images Based on Interval-Valued Fuzzy Sets

구간값 퍼지집합을 이용한 그레이 영상에서의 임계값 선택방법

  • 손창식 (대구가톨릭대학교 컴퓨터정보통신공학부) ;
  • 정환묵 (대구가톨릭대학교 컴퓨터정보통신공학부) ;
  • 서석태 (영남대학교 전기공학과) ;
  • 권순학 (영남대학교 전기공학과)
  • Published : 2007.08.25
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we propose a novel threshold selection method based on statistical information on gray-levels of given images and interval-valued fuzzy sets. In the proposed threshold selection method, the interval-valued fuzzy set is used to represent more definitely the relationship between a pixel and its belonging region, that is, the object and the background. Also the statistical information on gray-level is used to determine the rules and partitions of interval-valued fuzzy sets. To show the validity of the proposed method, we compared the performance of the proposed with those of conventional methods such as Otsu's method, Huang and Wang's method applied to 5 test images with various types of histograms.

본 논문에서는 주어진 영상의 그레이 레벨에 대한 통계적 정보와 구간값 퍼지집합에 기반을 둔 새로운 임계값 선택 방법을 제안한다. 제안한 임계값 선택 방법에서 구간값 퍼지집합은 영상의 픽셀과 그들이 속하는 영역, 즉 물체와 배경 간의 관계를 더욱 명확하게 나타내기 위해서 사용되고, 통계적 정보는 구간값 퍼지집합의 규칙과 파티션을 결정하기 위해서 이용된다. 제안한 방법의 타당성을 보이기 위해 다양한 형태의 히스토그램을 가진 5개의 테스트 영상들을 기존의 임계값 선택방법인 Otsu 방법과 Huang과 Wang의 방법과 비교하였다.

Keywords

References

  1. W. Doyle, 'Operation Useful for Similarity -Invariant Pattern Recognition,' Journal of the ACM, Vol. 9, no. 2, pp. 259-267, 1962 https://doi.org/10.1145/321119.321123
  2. A. Rosenfeld and A. C. Kak, Digital Picture Processing, 2nd Ed., Academic Press, 1982
  3. N. Otsu, 'A Threshold Selection Method from Gray-Level Histogram,' IEEE Tr. on Syst., Man, and Cybern., Vol. SMC-9, no. 1, pp. 62-66, 1979
  4. J. Kitter and J. Illingworth, 'Minimum Error Thresholding,' Pattern Recognition, Vol. 19, no. 1, pp. 41-47, 1986 https://doi.org/10.1016/0031-3203(86)90030-0
  5. L. K. Huang and M. J. Wang, 'Image Thresholding by Minimizing the Measures of Fuzziness,' Pattern Recognition, Vol. 28, no. 1, pp. 41-51, 1995 https://doi.org/10.1016/0031-3203(94)E0043-K
  6. H. D. Cheng, J. R. Chen, and H. Li, 'Thresholding Selection Based on Fuzzy c-Partition Entropy Approach,' Pattern Recognition, Vol. 31, no. 7, pp. 857-870, 1998 https://doi.org/10.1016/S0031-3203(97)00113-1
  7. H. D. Cheng and Y. H. Chen, 'Fuzzy Partition of Two-Dimensional Histogram and Its Application to Thresholding,' Pattern Recognition, Vol. 32, no. 5, pp. 825-843, 1999 https://doi.org/10.1016/S0031-3203(98)00080-6
  8. L. A. Zadeh, 'Fuzzy Sets, Information and Control,' Vol. 8, pp. 338-353, 1965 https://doi.org/10.1016/S0019-9958(65)90241-X
  9. I. B. Turksen, 'Interval-Valued Fuzzy Sets Based on Normal Forms,' Fuzzy Sets and Systems, Vol. 20, no. 2, pp. 191-210, 1986 https://doi.org/10.1016/0165-0114(86)90077-1
  10. M. B. Gorzalczany, 'A Method of Inference in Approximate Reasoning Based on Interval-Valued Fuzzy Sets,' Fuzzy Sets and Systems, Vol. 21, no. 1, pp. 1-17, 1987 https://doi.org/10.1016/0165-0114(87)90148-5
  11. M. B. Gorzalczany, 'An Interval-Valued Fuzzy Inference Method - Some Basic Properties,' Fuzzy Sets and Systems, Vol. 31, no. 2, pp. 243-251, 1989 https://doi.org/10.1016/0165-0114(89)90006-7