P-HAF: Homography Estimation using Partial Local Affine Frames

Daniel Barath

2017

Abstract

We propose an algorithm, called P-HAF, to estimate planar homographies using partially known local affine transformations. This general theory is able to exploit the affine components obtained by the commonly used partially affine covariant detectors, such as SIFT or SURF, in a real time capable way. P-HAF as a minimal solver can estimate the homography using two SIFT correspondences, moreover, it can deal with any number of point pairs as an overdetermined system. It is validated both on synthesized and publicly available datasets that exploiting all information leads to more accurate estimates and makes multi-homography estimation less ambiguous.

References

  1. Barath, D. and Hajder, L. (2016). Novel ways to estimate homography from local affine transformations. In In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, pages 432- 443.
  2. Barath, D., Hajder, L., and Matas, J. (2016a). Multi-h: Efficient recovery of tangent planes in stereo images. In BMVC 2016, 27th British Machine Vision Conference, 19-22 September, York, England, volume 28, page 32.
  3. Barath, D., Molnar, J., and Hajder, L. (2016b). Novel methods for estimating surface normals from affine transformations. In Computer Vision, Imaging and Computer Graphics Theory and Applications, pages 316- 337. Springer International Publishing.
  4. Bay, H., Tuytelaars, T., and Van Gool, L. (2006). SURF: Speeded up robust features. In European Conference on Computer Vision, pages 404-417. Springer.
  5. Bentolila, J. and Francos, J. M. (2014). Conic epipolar constraints from affine correspondences.Computer Vision and Image Understanding, 122:105-114.
  6. B ódis-Szomor ú, A., Riemenschneider, H., and Gool, L. V. (2014). Fast, approximate piecewise-planar modeling based on sparse structure-from-motion and superpixels. In IEEE Conference on Computer Vision and Pattern Recognition.
  7. Chen, J., Dixon, W. E., Dawson, D. M., and McIntyre, M. (2006). Homography-based visual servo tracking control of a wheeled mobile robot. Robotics, IEEE Transactions on, 22(2):406-415.
  8. Chuan, Z., Long, T. D., Feng, Z., and Li, D. Z. (2003). A planar homography estimation method for camera calibration. In Computational Intelligence in Robotics and Automation, 2003. Proceedings. 2003 IEEE International Symposium on, volume 1, pages 424-429. IEEE.
  9. Chum, O. and Matas, J. (2012). Homography estimation from correspondences of local elliptical features. In Pattern Recognition (ICPR), 2012 21st International Conference on, pages 3236-3239. IEEE.
  10. Courrieu, P. (2008). Fast computation of moore-penrose inverse matrices. arXiv preprint arXiv:0804.4809.
  11. Fischler, M. A. and Bolles, R. C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6):381-395.
  12. Furukawa, Y. and Ponce, J. (2010). Accurate, dense, and robust multi-view stereopsis. IEEE Trans. on Pattern Analysis and Machine Intelligence, 32(8):1362-1376.
  13. Hartley, R. I. and Zisserman, A. (2003). Multiple View Geometry in Computer Vision. Cambridge University Press.
  14. Isack, H. and Boykov, Y. (2012). Energy-based geometric multi-model fitting.International journal of computer vision, 97(2):123-147.
  15. Jain, P. K. and Jawahar, C. (2006). Homography estimation from planar contours. In 3D Data Processing, Visualization, and Transmission, Third International Symposium on, pages 877-884. IEEE.
  16. K öser, K. (2009). Geometric Estimation with Local Affine Frames and Free-form Surfaces. Shaker.
  17. K öser, K. and Koch, R. (2008). Differential spatial resection - pose estimation using a single local image feature. In ECCV, pages 312-325.
  18. Lowe, D. G. (1999). Object recognition from local scaleinvariant features. In Computer vision, 1999. The proceedings of the seventh IEEE international conference on, volume 2, pages 1150-1157. Ieee.
  19. Maronna, R., Martin, D., and Yohai, V. (2006). Robust statistics. John Wiley & Sons, Chichester. ISBN.
  20. Matas, J., Obdrzálek, S., and Chum, O. (2002). Local affine frames for wide-baseline stereo. In ICPR, Quebec, Canada, August 11-15, 2002., pages 363-366.
  21. Molnár, J. and Chetverikov, D. (2014). Quadratic transformation for planar mapping of implicit surfaces. Journal of Mathematical Imaging and Vision, 48:176-184.
  22. Moré, J. J. (1978). The levenberg-marquardt algorithm: implementation and theory. In Numerical analysis, pages 105-116. Springer.
  23. Prince, S. J., Xu, K., and Cheok, A. D. (2002). Augmented reality camera tracking with homographies. Computer Graphics and Applications, IEEE, 22(6):39-45.
  24. Raposo, C. and Barreto, J. P. (2016). Theory and practice of structure-from-motion using affine correspondences.
  25. Tanacs, A., Majdik, A., Molnar, J., Rai, A., and Kato, Z. (2014). Establishing correspondences between planar image patches. In Digital lmage Computing: Techniques and Applications (DlCTA), 2014 International Conference on, pages 1-7. IEEE.
  26. Ueshiba, T. and Tomita, F. (2003). Plane-based calibration algorithm for multi-camera systems via factorization of homography matrices. In Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on, pages 966-973. IEEE.
  27. Werner, T. and Zisserman, A. (2002). New techniques for automated architectural reconstruction from photographs. In Computer VisionECCV 2002, pages 541- 555. Springer.
  28. Wong, H. S., Chin, T.-J., Yu, J., and Suter, D. (2011). Dynamic and hierarchical multi-structure geometric model fitting. In International Conference on Computer Vision (ICCV).
  29. Zhang, Z. (2000). A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11):1330-1334.
  30. Zhang, Z. and Hanson, A. R. (1996). 3d reconstruction based on homography mapping. Proc. ARPA96, pages 1007-1012.
  31. Zhou, J. and Li, B. (2006). Robust ground plane detection with normalized homography in monocular sequences from a robot platform. In Image Processing, 2006 IEEE International Conference on, pages 3017-3020. IEEE.
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Paper Citation


in Harvard Style

Barath D. (2017). P-HAF: Homography Estimation using Partial Local Affine Frames . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-227-1, pages 227-235. DOI: 10.5220/0006130302270235


in Bibtex Style

@conference{visapp17,
author={Daniel Barath},
title={P-HAF: Homography Estimation using Partial Local Affine Frames},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={227-235},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006130302270235},
isbn={978-989-758-227-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)
TI - P-HAF: Homography Estimation using Partial Local Affine Frames
SN - 978-989-758-227-1
AU - Barath D.
PY - 2017
SP - 227
EP - 235
DO - 10.5220/0006130302270235