Abstract
Camera calibration is a process to find camera parameters. Camera parameter consists of intrinsic and extrinsic configuration and it is important to deal with the three-dimensional (3-D) geometry of the cameras and 3-D scene. However, camera calibration is quite annoying process when the number of cameras and images increase because it is operated by hand to indicate exact points. In order to eliminate the inconvenience of a manual manipulation, we propose a new pattern feature detection algorithm. The proposed method employs the Harris corner detector to find the candidate for the pattern feature points in images. Among them, we extract valid pattern feature points by using a circular sample. Test results show that this algorithm can provide reasonable camera parameters compared to camera parameters using the Matlab calibration toolbox by hand but eliminated a burden of manual operation.
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Acknowledgements
This research was supported by the ‘Cross-Ministry Giga KOREA Project’ of the Ministry of Science, ICT and Future Planning, Republic of Korea(ROK). [GK15C0100, Development of Interactive and Realistic Massive Giga-Content Technology]
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© 2015 Springer International Publishing Switzerland
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Shin, DW., Ho, YS. (2015). Pattern Feature Detection for Camera Calibration Using Circular Sample. In: Ho, YS., Sang, J., Ro, Y., Kim, J., Wu, F. (eds) Advances in Multimedia Information Processing -- PCM 2015. PCM 2015. Lecture Notes in Computer Science(), vol 9315. Springer, Cham. https://doi.org/10.1007/978-3-319-24078-7_62
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DOI: https://doi.org/10.1007/978-3-319-24078-7_62
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