Sensors 2009, 9(6), 4572-4585; doi:10.3390/s90604572
Article

3D Vision by Using Calibration Pattern with Inertial Sensor and RBF Neural Networks

Received: 17 April 2009; in revised form: 13 May 2009 / Accepted: 5 June 2009 / Published: 11 June 2009
(This article belongs to the Special Issue Neural Networks and Sensors)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: Camera calibration is a crucial prerequisite for the retrieval of metric information from images. The problem of camera calibration is the computation of camera intrinsic parameters (i.e., coefficients of geometric distortions, principle distance and principle point) and extrinsic parameters (i.e., 3D spatial orientations: ω, φ, κ, and 3D spatial translations: tx, ty, tz). The intrinsic camera calibration (i.e., interior orientation) models the imaging system of camera optics, while the extrinsic camera calibration (i.e., exterior orientation) indicates the translation and the orientation of the camera with respect to the global coordinate system. Traditional camera calibration techniques require a predefined mathematical-camera model and they use prior knowledge of many parameters. Definition of a realistic camera model is quite difficult and computation of camera calibration parameters are error-prone. In this paper, a novel implicit camera calibration method based on Radial Basis Functions Neural Networks is proposed. The proposed method requires neither an exactly defined camera model nor any prior knowledge about the imaging-setup or classical camera calibration parameters. The proposed method uses a calibration grid-pattern rotated around a static-fixed axis. The rotations of the calibration grid-pattern have been acquired by using an Xsens MTi-9 inertial sensor and in order to evaluate the success of the proposed method, 3D reconstruction performance of the proposed method has been compared with the performance of a traditional camera calibration method, Modified Direct Linear Transformation (MDLT). Extensive simulation results show that the proposed method achieves a better performance than MDLT aspect of 3D reconstruction.
Keywords: camera calibration; MDLT; differential evolution; RBF neural networks
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MDPI and ACS Style

Beşdok, E. 3D Vision by Using Calibration Pattern with Inertial Sensor and RBF Neural Networks. Sensors 2009, 9, 4572-4585.

AMA Style

Beşdok E. 3D Vision by Using Calibration Pattern with Inertial Sensor and RBF Neural Networks. Sensors. 2009; 9(6):4572-4585.

Chicago/Turabian Style

Beşdok, Erkan. 2009. "3D Vision by Using Calibration Pattern with Inertial Sensor and RBF Neural Networks." Sensors 9, no. 6: 4572-4585.

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