Volume 8 Number 6 (Dec. 2016)
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IJCEE 2016 Vol.8(6): 304-318 ISSN: 1793-8163
DOI: 10.17706/IJCEE.2016.8.6.304-318

An Intelligent Binocular Vision System with Zoom Lens for 3D Scene Reconstruction

Shuyang Dou, Hiroshi Nagahashi
Abstract—In this paper, we propose an intelligent binocular vision system for 3D scene reconstruction by using zoom lens. Unlike many existing methods which use precalibrated fixed focal length lens, our system allows users to change zoom and focus settings accordingly while taking photos. Thus, more details about some interesting parts of a scene can be captured clearly. In addition, using a zoom lens is advantageous especially when the system can only be moved within some small areas. After changing camera’s zoom or focus setting, its intrinsic parameters will be changed. It is important to calibrate these parameters accurately in order to reconstruct the 3D scene model successfully. We propose an efficient camera calibration method which can dynamically compute the focal length and principle point. It is proved that this method could generate more accurate estimations than the linear interpolation method. We introduced a criterion in our system which can control the zoom lens properly. Our system also contains a strategy to detect and remove redundant photos, which can speed up the computation. Experimental results show that our proposed system could create more plausible 3D models.

Index Terms—Binocular vision system, camera calibration, Zoom lens, 3D scene reconstruction.

Shuyang Dou is with Tokyo Institute of Technology, Department of Information Processing, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa, Japan, 226-8503.
Hiroshi Nagahashi is with Tokyo Institute of Technology, Laboratory for Future Interdisciplinary Research of Science and Technology (FIRST), 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa, Japan, 226-8503.

Cite:Shuyang Dou, Hiroshi Nagahashi, "An Intelligent Binocular Vision System with Zoom Lens for 3D Scene Reconstruction," International Journal of Computer and Electrical Engineering vol. 8, no. 5, pp. 304-318, 2016.

General Information

ISSN: 1793-8163
Frequency: Semiyearly
Editor-in-Chief: Prof. Yucong Duan
Abstracting/ Indexing: EI (INSPEC, IET), Ulrich's Periodicals Directory, Google Scholar, EBSCO, Engineering & Technology Digital Library, ProQuest, and Electronic Journals Library
E-mail: ijcee@iap.org

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