Paper
23 January 2023 Analysis of parallax characteristics of binocular vision based on no-similar imaging
Deng Chao, Yichao Chen, Zheng Junxi, Shi Guowei, Wang Chuyan
Author Affiliations +
Proceedings Volume 12557, AOPC 2022: Optical Sensing, Imaging, and Display Technology; 125572P (2023) https://doi.org/10.1117/12.2653233
Event: Applied Optics and Photonics China 2022 (AOPC2022), 2022, Beijing, China
Abstract
The important characteristic of binocular vision system is that it can form stereo parallax. Subject to the limitation of "no-similar" imaging principle, the parallax of object points in binocular vision with large FOV is not necessarily distinguishable by the detector, but the distinguishable parallax is the basis of realizing stereo vision. Based on the ideal theoretical model, this section will establish a general structure of large FOV binocular stereo vision parallax model, learn from the relevant concepts of human binocular vision, conduct theoretical research on the characteristics of large FOV binocular stereo vision and the influence of system parameters, analyze the characteristics of image parallax, and compare it with the image parallax formed by small FOV binocular vision. This paper also simulates the generation of parallax in large FOV binocular vision image to verify the correctness of the theoretical analysis. The research of this paper plays a role in expanding the application of binocular vision with large FOV, and can also provide reference for the design of system parameters.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Deng Chao, Yichao Chen, Zheng Junxi, Shi Guowei, and Wang Chuyan "Analysis of parallax characteristics of binocular vision based on no-similar imaging", Proc. SPIE 12557, AOPC 2022: Optical Sensing, Imaging, and Display Technology, 125572P (23 January 2023); https://doi.org/10.1117/12.2653233
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KEYWORDS
Imaging systems

Cameras

Stereo vision systems

Eye

Analytical research

Visual process modeling

Radon

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