Per-Pixel Water Detection on Surfaces with Unknown Reflectance
Per-Pixel Water Detection on Surfaces with Unknown Reflectance
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DOI：
10.1587/transinf.2021pcp0002
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发表时间：
2021-10
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影响因子：
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通讯作者：
Chao Wang;Michihiko Okuyama;Ryo Matsuoka;Takahiro Okabe
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文献类型：
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作者：
Chao Wang;Michihiko Okuyama;Ryo Matsuoka;Takahiro Okabe
摘要水检测对于机器视觉应用（如视觉检测和机器人运动规划）非常重要。在本文中，我们提出了一种方法，每像素的水检测未知表面的高光谱图像。我们提出的方法是基于水的光谱特性：水对于可见光是透明的，但是对于近红外光是半透明/不透明的，因此当水存在于表面上时，表面的表观近红外光谱反射率小于原始表面的表观近红外光谱反射率。具体地，我们使用少量基向量的线性组合来近似光谱反射率，并估计原始的近-红外线反射率与可见光反射率（其不取决于水的存在或不存在）的差来检测水。通过对真实的图像的实验表明，本文提出的基于可见光光谱反射率的近红外光谱反射率估计方法比现有方法具有更好的性能。
SUMMARY Water detection is important for machine vision applications such as visual inspection and robot motion planning. In this paper, we propose an approach to per-pixel water detection on unknown surfaces with a hyperspectral image. Our proposed method is based on the water spectral characteristics: water is transparent for visible light but translucent / opaque for near-infrared light and therefore the apparent near-infrared spectral re-flectance of a surface is smaller than the original one when water is present on it. Specifically, we use a linear combination of a small number of basis vector to approximate the spectral reflectance and estimate the original near-infrared reflectance from the visible reflectance (which does not depend on the presence or absence of water) to detect water. We conducted a number of experiments using real images and show that our method, which estimates near-infrared spectral reflectance based on the visible spectral re-flectance, has better performance than existing techniques.