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Achievement of Color Constancy by Eigenvector

고유벡터에 의한 색 일관성의 달성

  • Kim, Dal-Hyoun (Dept. of Computer Engineering, ChungBuk University) ;
  • Bak, Jong-Cheon (Dept. of Computer Engineering, ChungBuk University) ;
  • Jung, Seok-Ju (Dept. of Computer Engineering, ChungBuk University) ;
  • Kim, Kyung-Ah (Dept. of Biomedical Engineering, College of Medicine, ChungBuk University) ;
  • Cha, Eun-Jong (Dept. of Biomedical Engineering, College of Medicine, ChungBuk University) ;
  • Jun, Byoung-Min (Dept. of Computer Engineering, ChungBuk University)
  • 김달현 (충북대학교 컴퓨터공학과) ;
  • 박종천 (충북대학교 컴퓨터공학과) ;
  • 정석주 (충북대학교 컴퓨터공학과) ;
  • 김경아 (충북대학교 의과대학 의공학과) ;
  • 차은종 (충북대학교 의과대학 의공학과) ;
  • 전병민 (충북대학교 컴퓨터공학과)
  • Published : 2009.05.31

Abstract

In order to achieve color constancy, this paper proposes a method that can detect an invariant direction that affects formation of an intrinsic image significantly, using eigenvector in the $\chi$-chromaticity space. Firstly, image is converted into datum in the $\chi$-chromaticity space which was suggested by Finlayson et al. Secondly, it removes datum, like noises, with low probabilities that may affect an invariant direction. Thirdly, so as to detect the invariant direction that is consistent with a principal direction, the eigenvector corresponding to the largest eigenvalue is calculated from datum extracted above. Finally, an intrinsic image is acquired by recovering datum with the detected invariant direction. Test images were used as parts of the image data presented by Barnard et al., and detection performance of invariant direction was compared with that of entropy minimization method. The results of experiment showed that our method detected constant invariant direction since the proposed method had lower standard deviation than the entropy method, and was over three times faster than the compared method in the aspect of detection speed.

본 논문은 색 일관성을 달성하기 위해 $\chi$-색도 공간에서 고유벡터를 이용하여 본질 영상의 획득에 중대한 영향을 미치는 불변 방향을 검출하는 알고리즘을 제안한다. 이를 위해, 우선 영상을 Finlayson 등이 제안한 방법을 활용하여 $\chi$-색도 공간으로 변환한다. 두 번째로, 불변 방향에 영향을 줄 수 있는 잡음 같은 낮은 빈도를 갖는 데이터들을 제거한다. 세 번째로, 주축 방향과 일치하는 불변 방향을 검출하기 위해, 위 단계에서 추출된 데이터들로부터 가장 큰 고유값에 해당하는 고유벡터를 계산한다. 마지막으로, 검출된 불변 방향을 사용하여 복원함으로써, 본질 영상을 획득한다. 실험 영상은 Barnard 등이 사용한 영상 데이터들 중 일부를 사용하였고, 불변 방향의 검출 성능은 엔트로피 최소화 기법과 비교되었다. 실험 결과, 제안한 기법은 기존 기법에 비해 표준편차가 낮아 불변 방향이 일정하게 검출되었으며, 시간적 측면에서 기존의 기법에 비해 3배 이상 효율적이었다.

Keywords

References

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