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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학위논문
- 저자정보
- 지도교수
- 하영호
- 발행연도
- 2013
- 저작권
- 경북대학교 논문은 저작권에 의해 보호받습니다.
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Generally an image acquired from traditional digital camera consists of 3-channel, such as red, green, and blue. However, an RGB image cannot fully represent real scene. To accurately represent colors in the real scene, a multi-channel camera system is necessary. One of researches related to multi-channel camera system is a spectral reflectance estimation from acquired images. The most common methods for the spectral reflectance estimation are Wiener estimation, principal component analysis, and pseudo-inverse computation. Among these methods, Wiener estimation is widely used. While highly simple and accurate in controlled conditions, Wiener estimation does not perform as well with real scene data. Therefore, an adaptive Wiener estimation has been proposed for the improving performance over the conventional Wiener estimation. The adaptive Wiener estimation uses a similar training set that was adaptively constructed from the standard training set according to the camera response. In this paper, a new method of constructing such a similar training set using correlation coefficient between spectral reflectance in the standard training set and the first approximation of the spectral reflectance that was obtained by Wiener estimation is proposed. The experimental results showed that the proposed method was more accurate than the conventional Wiener estimations.
목차
Ⅰ. 서론 ····································································· 1Ⅱ. 다채널 카메라 시스템 ················································ 4Ⅲ. 위너 추정법 ···························································· 7Ⅳ. 적응적 위너 추정법 ··················································· 134.1 거리를 이용한 유사 모집단 기반의 적응적 위너 추정법 ······ 154.2 제안된 적응적 위너 추정법 ······································· 26Ⅴ. 실험 및 결과 ··························································· 37Ⅵ. 결론 ····································································· 48참 고 문 헌 ··························································· 49영 문 초 록 ··························································· 52
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