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Proceedings Paper

Fast-camera calibration of stereo vision system using BP neural networks
Author(s): Huimin Cai; Kejie Li; Meilian Liu; Ping Song
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Paper Abstract

In position measurements by far-range photogrammetry, the scale between object and image has to be calibrated. It means to get the parameters of the perspective projection matrix. Because the image sensor of fast-camera is CMOS, there are many uncertain distortion factors. It is hard to describe the scale between object and image for the traditional calibration based on the mathematical model. In this paper, a new method for calibrating stereo vision systems with neural networks is described. A linear method is used for 3D position estimation and its error is corrected by neural networks. Compared with DLT (Direct Linear Transformation) and direct mapping by neural networks, the accuracy is improved. We have used this method in the drop point measurement of an object in high speed successfully.

Paper Details

Date Published: 23 October 2010
PDF: 7 pages
Proc. SPIE 7658, 5th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Detector, Imager, Display, and Energy Conversion Technology, 76585B (23 October 2010); doi: 10.1117/12.865933
Show Author Affiliations
Huimin Cai, Beijing Institute of Technology (China)
Kejie Li, Beijing Institute of Technology (China)
Meilian Liu, Beijing Institute of Technology (China)
Beijing Technology and Business Univ. (China)
Ping Song, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 7658:
5th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Detector, Imager, Display, and Energy Conversion Technology
Yadong Jiang; Bernard Kippelen; Junsheng Yu, Editor(s)

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