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  1. 学会・会議発表論文
  2. 学会・会議発表論文

Estimating Absorption Coefficient from a Single Image via Entropy Minimization

http://hdl.handle.net/10228/0002001015
http://hdl.handle.net/10228/0002001015
9f868a33-e3b3-4f86-a9d4-4faf8dfb46f4
名前 / ファイル ライセンス アクション
10441844.pdf 10441844.pdf (4.6 MB)
アイテムタイプ 学術雑誌論文 = Journal Article(1)
公開日 2024-11-07
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_5794
資源タイプ conference paper
タイトル
タイトル Estimating Absorption Coefficient from a Single Image via Entropy Minimization
言語 en
言語
言語 eng
著者 Katahira, Junya

× Katahira, Junya

en Katahira, Junya

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Kawahara, Ryo

× Kawahara, Ryo

en Kawahara, Ryo

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Okabe, Takahiro

× Okabe, Takahiro

en Okabe, Takahiro

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抄録
内容記述タイプ Abstract
内容記述 When light passes through a liquid, its energy is attenuated due to absorption. The attenuation depends on both the spectral absorption coefficient of a liquid and the optical path length of light, and is described by the Lambert-Beer law. The spectral absorption coefficients of liquids are often unknown in real-world applications and to be measured/estimated in advance, because they depend not only on liquid media themselves but also on dissolved materials. In this paper, we propose a method for estimating the three-band (RGB) absorption coefficient of a liquid only from a single color image of an under-liquid scene taken from the outside of the liquid in a passive and non-contact manner. Specifically, our proposed method investigates the observed colors in the log of chromaticity band-ratio space, and estimates the absorption coefficient up to a certain ambiguity via entropy minimization. Moreover, we reveal the effects of the ambiguity on the applications to under-liquid image/scene analysis. We conducted a number of experiments using real images, and confirmed that our method works well and is useful for under-liquid shape recovery, absorption removal, and reflectance recovery.
言語 en
備考
内容記述タイプ Other
内容記述 34th British Machine Vision Conference, BMVC 2023, 20 - 24 November, 2023, Aberdeen, UK
書誌情報
発行日 2023-11
URI
識別子タイプ URI
関連識別子 https://proceedings.bmvc2023.org/
著作権関連情報
権利情報 Copyright (c) 2023. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.
出版タイプ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
査読の有無
値 yes
研究者情報
URL https://hyokadb02.jimu.kyutech.ac.jp/html/100001422_ja.html
論文ID(連携)
値 10441844
連携ID
値 12418
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