The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
물 감지는 육안 검사 및 로봇 동작 계획과 같은 머신 비전 애플리케이션에 중요합니다. 본 논문에서는 초분광 이미지를 사용하여 알려지지 않은 표면의 픽셀당 물 감지에 대한 접근 방식을 제안합니다. 우리가 제안한 방법은 물 스펙트럼 특성을 기반으로 합니다. 물은 가시광선에서는 투명하지만 근적외선에서는 반투명/불투명합니다. 따라서 표면의 겉보기 근적외선 스펙트럼 반사율은 물이 존재할 때 원래 표면보다 작습니다. . 구체적으로, 우리는 소수의 기본 벡터의 선형 조합을 사용하여 분광 반사율을 근사화하고 가시 반사율(물 유무에 의존하지 않음)로부터 원래의 근적외선 반사율을 추정하여 물을 감지합니다. 실제 영상을 이용하여 여러 실험을 진행한 결과, 가시광선 분광반사율을 기반으로 근적외선 분광반사율을 추정하는 방법이 기존 기법보다 우수한 성능을 보인다는 것을 보여주었다.
Chao WANG
Kyushu Institute of Technology
Michihiko OKUYAMA
Kyushu Institute of Technology
Ryo MATSUOKA
The University of Kitakyushu
Takahiro OKABE
Kyushu Institute of Technology
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부
Chao WANG, Michihiko OKUYAMA, Ryo MATSUOKA, Takahiro OKABE, "Per-Pixel Water Detection on Surfaces with Unknown Reflectance" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 10, pp. 1555-1562, October 2021, doi: 10.1587/transinf.2021PCP0002.
Abstract: 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 reflectance 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 reflectance, has better performance than existing techniques.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021PCP0002/_p
부
@ARTICLE{e104-d_10_1555,
author={Chao WANG, Michihiko OKUYAMA, Ryo MATSUOKA, Takahiro OKABE, },
journal={IEICE TRANSACTIONS on Information},
title={Per-Pixel Water Detection on Surfaces with Unknown Reflectance},
year={2021},
volume={E104-D},
number={10},
pages={1555-1562},
abstract={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 reflectance 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 reflectance, has better performance than existing techniques.},
keywords={},
doi={10.1587/transinf.2021PCP0002},
ISSN={1745-1361},
month={October},}
부
TY - JOUR
TI - Per-Pixel Water Detection on Surfaces with Unknown Reflectance
T2 - IEICE TRANSACTIONS on Information
SP - 1555
EP - 1562
AU - Chao WANG
AU - Michihiko OKUYAMA
AU - Ryo MATSUOKA
AU - Takahiro OKABE
PY - 2021
DO - 10.1587/transinf.2021PCP0002
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2021
AB - 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 reflectance 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 reflectance, has better performance than existing techniques.
ER -