Ronda Prieto, José Ignacio and Valdés Morales, Antonio and Gallego Bonet, Guillermo (2011) Autocalibration with the Minimum Number of Cameras with Known Pixel Shape. International Journal of Computer Vision . ISSN 0920-5691 (Print) 1573-1405 (Online) (Submitted)
| PDF 5Mb |
Abstract
We address the problem of the Euclidean upgrading of a projective calibration of a minimal set of cameras with known pixel shape and otherwise arbitrarily varying intrinsic and extrinsic parameters. To this purpose, we introduce as our basic geometric tool the six-line conic variety (SLCV), consisting in the set of planes intersecting six given lines of 3D space in points of a conic. We show that the set of solutions of the Euclidean upgrading problem for three cameras with known pixel shape can be parameterized in a computationally efficient. As a consequence, we propose an algorithm that performs a Euclidean upgrading with 5 ({theoretical minimum}) or more cameras with the knowledge of the pixel shape as the only constraint. We provide experiments with real images showing the good performance of the technique.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Camera autocalibration, Varying parameters, Square pixels, Three-dimensional reconstruction, Absolute Conic, Six Line Conic Variety |
| Subjects: | Sciences > Computer science > Artificial intelligence |
| ID Code: | 14615 |
| References: | 1. Bôcher, M.: Introduction to Higher Algebra. Dover Phoenix Editions. Dover Publications (2004) 2. Bradski, G.: The OpenCV Library. Dr. Dobb’s Journal of Software Tools (2000) 3. Carballeira, P., Ronda, J.I., Valdés, A.: 3d reconstruction with uncalibrated cameras using the six-line conic variety. In: ICIP, pp. 205–208. IEEE (2008) 4. Faugeras, O.: Stratification of 3-d vision: projective, affine, and metric representations. Journal of the Optical Society of America A 12, 46,548–4 (1995) 5. Furukawa, Y., Ponce, J.: Accurate, dense, and robust multi-view stereopsis. In: CVPR (2007) 6. Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, second edn. Cambridge University Press (2003) 7. Hartley, R.I.: Estimation of relative camera positions for uncalibrated cameras. In: Proc. European Conference on Computer Vision, pp. 579–587. Springer-Verlag, London, UK (1992) 8. Heyden, A., Åström, K.: Euclidean reconstruction from image sequences with varying and unknown focal length and principal point. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition. New York, USA (1997) 9. Lowe, D.: Object recognition from local scale-invariant features. In: ICCV, pp. 1150–1157 (1999) 10. Luong, Q.T., Viéville, T.: Canonical representations for the geometries of multiple projective views. Comput. Vis. Image Underst. 64, 193–229 (1996). DOI 10.1006/cviu.1996.0055. URL http://portal.acm.org/citation.cfm?id=235090.235091 11. Ma, Y., Soatto, S., Kosecka, J., Sastry, S.: An Invitation to 3-D Vision. Springer (2003) 12. Maybank, S.J., Faugeras, O.D.: A theory of self-calibration of a moving camera. Int. J. Comput. Vision 8(2), 123–151 (1992). DOI http://dx.doi.org/10.1007/BF00127171. URL http://portal.acm.org/citation.cfm?id=144382 13. Pollefeys, M., Gool, L.V.: A stratified approach to metric selfcalibration. In: Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 407–412 (1997). URL citeseer. ist.psu.edu/article/pollefeys97stratified.html 14. Ponce, J., McHenry, K., Papadopoulo, T., Teillaud, M., Triggs, B.: On the absolute quadratic complex and its application to autocalibration. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 780–787. Washington, DC, USA (2005). DOI http://dx.doi.org/10.1109/CVPR.2005.256 15. Ronda, J.I., Valdés, A., Gallego, G.: Line geometry and camera autocalibration. J. Math. Imaging Vis. 32(2), 193–214 (2008). DOI http://dx.doi.org/10.1007/s10851-008-0095-0 16. Schröcker, H.P.: Intersection conics of six straight lines. Beitr. Algebra Geom 46(2), 435–446 (2005) 17. Seo, Y., Heyden, A.: Auto-calibration from the orthogonality constraints. In: Proc. International Conference on Pattern Recognition, vol. 01, pp. 1067–1071. Los Alamitos, CA, USA (2000). DOI http://doi.ieeecomputersociety.org/10.1109/ICPR.2000.905277 18. Snavely, N., Seitz, S.M., Szeliski, R.: Modeling the world from internet photo collections. International Journal of Computer Vision 80(2), 189–210 (2008) 19. Sturm, P.: Critical motion sequences for monocular selfcalibration and uncalibrated euclidean reconstruction. Computer Vision and Pattern Recognition, IEEE Computer Society Conference on 0, 1100 (1997). DOI http://doi.ieeecomputersociety.org/10.1109/CVPR.1997.609467 20. Sturm, P.: Self-calibration of a moving zoom-lens camera by precalibration. Image and Vision Computing 15(8), 583–589 (1997). URL http://perception.inrialpes.fr/Publications/1997/Stu97b 21. Tresadern, P.A., Reid, I.D.: Camera calibration from human motion. Image Vision Comput. 26(6), 851–862 (2008). DOI http://dx.doi.org/10.1016/j.imavis.2007.10.001 22. Triggs, B.: Autocalibration and the absolute quadric. In: Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 609–614. Puerto Rico, USA (1997). URL http://perception.inrialpes.fr/Publications/1997/Tri97 23. Valdés, A., Ronda, J.I., Gallego, G.: Linear camera autocalibration with varying parameters. In: Proc. International Conference on Image Processing, vol. 5, pp. 3395–3398. Singapore (2004) 24. Valdés, A., Ronda, J.I., Gallego, G.: The absolute line quadric and camera autocalibration. International Journal of Computer Vision 66(3), 283–303 (2006 |
| Deposited On: | 07 Mar 2012 16:45 |
| Last Modified: | 07 Mar 2012 16:45 |
Repository Staff Only: item control page



