Authors:
Kenta Kageyama
;
Ryo Kawahara
and
Takahiro Okabe
Affiliation:
Department of Artificial Intelligence, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
Keyword(s):
Spectral Imaging, Passive Measurement, Spectral Absorption Coefficient, Matrix Factorization.
Abstract:
When light passes through a liquid, its energy is attenuated due to absorption. The attenuation depends both on the spectral absorption coefficient of a liquid and on 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 spectral absorption coefficient of a liquid only from two-view hyperspectral images of an under-liquid scene taken from the outside of the liquid in a passive and non-contact manner. Specifically, we show that the estimation results in Non-negative Matrix Factorization (NMF) because both the objective variables and the explanatory variables are all nonnegative, and then study the ambiguity in matrix factorization. We conducted a number of experiments using real hyperspectra
l images, and confirmed that our method works well and is useful for reconstructing shape of an under-liquid scene.
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