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The Mechanistic Deconvolutive Image Sensor Model for an Arbitrary Pan–Tilt Plane of View

Authors: S. H. Lim, T. Furukawa


This paper presents a generalized form of the mechanistic deconvolution technique (GMD) to modeling image sensors applicable in various pan–tilt planes of view. The mechanistic deconvolution technique (UMD) is modified with the given angles of a pan–tilt plane of view to formulate constraint parameters and characterize distortion effects, and thereby, determine the corrected image data. This, as a result, does not require experimental setup or calibration. Due to the mechanistic nature of the sensor model, the necessity for the sensor image plane to be orthogonal to its z-axis is eliminated, and it reduces the dependency on image data. An experiment was constructed to evaluate the accuracy of a model created by GMD and its insensitivity to changes in sensor properties and in pan and tilt angles. This was compared with a pre-calibrated model and a model created by UMD using two sensors with different specifications. It achieved similar accuracy with one-seventh the number of iterations and attained lower mean error by a factor of 2.4 when compared to the pre-calibrated and UMD model respectively. The model has also shown itself to be robust and, in comparison to pre-calibrated and UMD model, improved the accuracy significantly.

Keywords: Calibration, Image sensor modeling, mechanistic deconvolution, lens distortion

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[1] F. Remondino and C. Fraser, "Digital camera calibrations:Considerations and comparisons," International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 36, no. 5, pp. 266-272, 2006.
[2] G.N. DeSouza and A.C. Kak "Vision for mobile robot navigation: A Survey," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 2, pp. 237-267, 2002.
[3] R.Y. Tsai, "A versatile camera calibration technique for high-Accuracy 3D machine vision metrology using off-the-Shelf TV cameras and lenses," IEEE Journal of Robotics and Automation, vol. Ra-3, no. 4, pp. 323-344, 1987.
[4] D.C. Brown, "Close-range camera calibration," Photogrammetric Engineering, vol. 37, no. 8, pp. 855-866, 1971.
[5] J. Weng, P. Cohen and M. Herniou, "Camera calibration with distortion models and accuracy evaluation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 10, pp. 965-980, 1992.
[6] S. Graf and T. Hanning, "Analytical solving radial distortion parameters," Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1104-1109, 2005.
[7] J. Mallon and P.F. Whelan, "Precise radial un-distortion of images," Proceedings of the 17th International Conference on Pattern Recognition, vol. 1, 2004.
[8] D.G. Bailey, "A new approach to lens distortion correction," Proceedings Image and Vision Computing New Zealand, pp. 59-64,2002.
[9] S. Park and K. Hong, "Practical ways to calculate camera lens distortion for real-time camera calibration," Pattern Recognition, vol. 34, no. 6, pp. 1199-1206, 2004.
[10] S.J. Maybank and O.D. Faugeras, "A theory of self-calibration of a moving camera," International Journal of Computer Vision, vol. 8, no. 2, pp. 123-151, 1992.
[11] E.E. Hemayed, "A survey of camera self-calibration," Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, pp. 352-357, 2003.
[12] Z. Zhang, "A flexible new technique for camera calibration," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 11, pp. 1330-1334, 2000.
[13] S. H. Lim, T. Furukawa, "Calibration-free image sensor modelling using mechanistic deconvolution," Sensors and Transducers Journal, vol. 90, Special Issue, pp. 195-208, April 2008.