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논문 기본 정보

자료유형
학술대회자료
저자정보
Hideaki Uchino (Kyushu Institute of Technology) Hyun-Woo Kim (Kyushu Institute of Technology) Myungjin Cho (Hankyong National University) Min-Chul Lee (Kyushu Institute of Technology)
저널정보
제어로봇시스템학회 제어로봇시스템학회 국제학술대회 논문집 ICCAS 2023
발행연도
2023.10
수록면
1,465 - 1,470 (6page)

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초록· 키워드

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In this paper, we propose a method for visualizing object information under low light conditions using photon-counting integral imaging and depth images as prior information. To visualize 3D objects under low-light conditions, computational photon-counting imaging may be used. It is possible to estimate photons using statistical methods such as the Poisson distribution. Therefore, photon-counting integral imaging can visualize 3D images under low-light conditions by maximum likelihood estimation (MLE). However, MLE does not consider prior information so it may not be an accurate estimation. In addition, it is difficult to obtain accurate depth information as the distance increases due to rounding errors caused by computational reconstruction. Therefore, we propose Bayesian estimation such as a maximum a posteriori estimation method that uses as a priori information images from a depth camera, which can be acquired even in low-light conditions, to improve the quality of 3D images. To evaluate image quality of the proposed method, optical experiments were conducted by calculating image quality metrics. This technology can be utilized for night vision applications, such as object recognition in security camera systems and autonomous driving, using RGB cameras and depth cameras.

목차

Abstract
1. INTRODUCTION
2. THEORY
3. EXPERIMENTAL SETUP AND RESULT
5. CONCLUSION
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