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Interpreting Sphere Images Using the Double-Contact Theorem

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Book cover Computer Vision – ACCV 2006 (ACCV 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3851))

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Abstract

An occluding contour of a sphere is projected to a conic in the perspective image, and such a conic is called a sphere image. Recently, it has been discovered that each sphere image is tangent to the image of the absolute conic at two double-contact image points. The double-contact theorem describes the properties of three conics which all have double contact with another conic. This theorem provides a linear algorithm to find the another conic if these three conics are given. In this paper, the double-contact theorem is employed to interpret the properties among three sphere images and the image of the absolute conic. The image of the absolute conic can be determined from three sphere images using the double-contact theorem. Therefore, a linear calibration method using three sphere images is obtained. Only three sphere images are required, and all five intrinsic parameters are recovered linearly without making assumptions, such as, zero-skew or unitary aspect ratio. Extensive experiments on simulated and real data were performed and shown that our calibration method is an order of magnitude faster than previous optimized methods and a little faster than former linear methods while maintaining comparable accuracy.

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References

  1. Agrawal, M., Davis, L.S.: Camera Calibration using Spheres: A Semi-definite Programming Approach. In: Proc. Ninth Int’l Conf. Computer Vision, pp. 782–791 (2003)

    Google Scholar 

  2. Daucher, N., Dhome, M., Lapreste, J.: Camera Calibration from Spheres Images. In: Proc. European Conf. Computer Vision, pp. 449–454 (1994)

    Google Scholar 

  3. Evelyn, C., et al.: The Seven Circles Theorem and Other New Theorems. Stacey International, London (1974)

    MATH  Google Scholar 

  4. Fitzgibbon, A., Pilu, M., Fisher, R.: Direct Least Square Fitting of Ellipses. IEEE Trans. Pattern Analysis and Machine Intelligence 21(5), 476–480 (1999)

    Article  Google Scholar 

  5. Hartley, R.: An Algorithm for Self-calibration from Several Views. In: Proc. IEEE. Conf. Computer Vision and Pattern Recognition, pp. 908–912 (1994)

    Google Scholar 

  6. Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2000)

    MATH  Google Scholar 

  7. Maybank, S., Faugeras, O.: A Theory of Self-calibration of a Moving Camera. Int’l J. Computer Vision 8(2), 123–151 (1992)

    Article  Google Scholar 

  8. Penna, M.: Camera Calibration: A Quick and Easy Way to Determine the Scale Factor. IEEE Trans. Pattern Analysis and Machine Intelligence 13(12), 1240–1245 (1991)

    Article  Google Scholar 

  9. Pollefeys, M., Koch, R., Van Gool, L.: Selfcalibration and Metric Reconstruction in Spite of Varying and Unknown Internal Camera Parameters. In: Proc. Sixth Int’l Conf. Computer Vision, pp. 90–95 (1998)

    Google Scholar 

  10. Semple, J., Kneebone, G.: Algebraic Projective Geometry. Oxford Science Publication (1952)

    Google Scholar 

  11. Sturm, P., Maybank, S.: On Plane-based Camera Calibration: A General Algorithm, Singularities and Applications. In: Proc. IEEE. Conf. Computer Vision and Pattern Recognition, pp. 432–437 (1999)

    Google Scholar 

  12. Teramoto, H., Xu, G.: Camera Calibration by a Single Image of Balls: From Conics to the Absolute Conic. In: Proc. Asian Conf. Computer Vision, pp. 499–506 (2002)

    Google Scholar 

  13. Tsai, R.Y.: A Versatile Camera Calibration Technique for High-accuracy 3D Machine Vision Metrology using Off-the-shelf TV Cameras and Lenses. IEEE J. Robotics and Automation 3(4), 323–344 (1987)

    Article  Google Scholar 

  14. Ying, X., Hu, Z.: Catadioptric Camera Calibration Using Geometric Invariants. IEEE Trans. Pattern Analysis and Machine Intelligence 26(10), 1260–1271 (2004)

    Article  Google Scholar 

  15. Ying, X., Zha, H.: Linear Approaches to Camera Calibration from Sphere Images or Active Intrinsic Calibration using Vanishing Points. In: Proc. Tenth Int’l Conf. Computer Vision, pp. 596–603 (2005)

    Google Scholar 

  16. Zhang, Z.: Flexible Camera Calibration by Viewing Planes from Unknown Orientations. In: Proc. Seventh Int’l Conf. Computer Vision, pp. 666–673 (1999)

    Google Scholar 

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© 2006 Springer-Verlag Berlin Heidelberg

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Ying, X., Zha, H. (2006). Interpreting Sphere Images Using the Double-Contact Theorem. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3851. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612032_73

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  • DOI: https://doi.org/10.1007/11612032_73

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-31219-2

  • Online ISBN: 978-3-540-32433-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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