skip to main content
10.1145/2425836.2425877acmotherconferencesArticle/Chapter ViewAbstractPublication PagesivcnzConference Proceedingsconference-collections
poster

Evaluation of feature detectors for registering aerial images

Published:26 November 2012Publication History

ABSTRACT

The detection, extraction, and matching of image features is a popular method for generating point-to-point correspondences for the estimation of scene and camera geometries. In this work we evaluate the performance of a variety of feature detection algorithms over two reference data sets and a set of aerial images which includes large changes in scene illumination. The evaluated detectors showed expected performance against the reference data sets, and aerial images with constant lighting conditions, but were unsuccessful in aligning image pairs showing strong changes in image exposure and illumination.

References

  1. M. Agrawal, K. Konolige, and M. R. Blas. CenSurE: center surround extremas for realtime feature detection and matching. In D. Forsyth, P. Torr, and A. Zisserman, editors, Computer Vision -- ECCV 2008, volume 5305, pages 102--115. Springer Berlin Heidelberg, Berlin, Heidelberg, 2008.Google ScholarGoogle Scholar
  2. H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool. Speeded-up robust features (SURF). Computer Vision and Image Understanding, 110(3): 346--359, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. T. Botterill. Visual Navigation for Mobile Robots using the Bag-of-Words Algorithm. PhD thesis, University of Canterbury, Christchurch, New Zealand, 2010.Google ScholarGoogle Scholar
  4. T. Botterill, S. Mills, and R. Green. New conditional sampling strategies for speeded-up RANSAC. In Proceedings of the British Machine Vision Conference, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  5. O. Chum and J. Matas. Matching with PROSAC -- progressive sample consensus. In Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 220--226, San Diego, CA, USA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. K. Cordes, B. Rosenhahn, and J. Ostermann. Increasing the accuracy of feature evaluation benchmarks using differential evolution. In Differential Evolution (SDE), 2011 IEEE Symposium on, pages 1--8. IEEE, Apr. 2011.Google ScholarGoogle ScholarCross RefCross Ref
  7. C. Engels, H. Stewénius, and D. Nistér. Bundle adjustment rules. Photogrammetric Computer Vision, 2, 2006.Google ScholarGoogle Scholar
  8. C. Evans. Notes on the OpenSURF library. Technical Report CSTR-09-001, University of Bristol, Jan. 2009.Google ScholarGoogle Scholar
  9. S. Gauglitz, T. Höllerer, and M. Turk. Evaluation of interest point detectors and feature descriptors for visual tracking. International Journal of Computer Vision, 94(3): 335--360, Mar. 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. J. Geusebroek, R. van den Boomgaard, A. Smeulders, and H. Geerts. Color invariance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(12): 1338--1350, Dec. 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. C. Harris and M. Stephens. A combined corner and edge detector. In Alvey vision conference, volume 15, page 50, 1988.Google ScholarGoogle Scholar
  12. R. I. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, ISBN: 0521540518, second edition, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. R. Hess. An open-source SIFT Library. In Proceedings of the international conference on Multimedia, pages 1493--1496, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. D. G. Lowe. Distinctive image features from Scale-Invariant keypoints. International Journal of Computer Vision, 60(2): 91--110, Nov. 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. K. Mikolajczyk and C. Schmid. An affine invariant interest point detector. In A. Heyden, G. Sparr, M. Nielsen, and P. Johansen, editors, Computer Vision -- ECCV 2002, volume 2350, pages 128--142. Springer Berlin Heidelberg, Berlin, Heidelberg. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, and L. V. Gool. A comparison of affine region detectors. International Journal of Computer Vision, 65(--2): 43--72, Oct. 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. OpenCV development team. OpenCV v2.3 documentation. Technical report, Aug. 2011.Google ScholarGoogle Scholar
  18. E. Rosten and T. Drummond. Machine learning for High-Speed corner detection. In A. Leonardis, H. Bischof, and A. Pinz, editors, Computer Vision -- ECCV 2006, volume 3951, pages 430--443. Springer Berlin Heidelberg, Berlin, Heidelberg, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. E. Rosten, R. Porter, and T. Drummond. Faster and better: A machine learning approach to corner detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(1): 105--119, Jan. 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. A. Schmidt, M. Kraft, and A. Kasiński. An evaluation of image feature detectors and descriptors for robot navigation. In L. Bolc, R. Tadeusiewicz, L. J. Chmielewski, and K. Wojciechowski, editors, Computer Vision and Graphics, volume 6375, pages 251--259. Springer Berlin Heidelberg, Berlin, Heidelberg, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. J. Shi and C. Tomasi. Good features to track. In Proceedings of the 1994 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1994, pages 593--600. IEEE Comput. Soc. Press, 1994.Google ScholarGoogle Scholar
  22. C. Tomasi and T. Kanade. Detection and tracking of point features, 1991.Google ScholarGoogle Scholar
  23. P. Torr. MLESAC: a new robust estimator with application to estimating image geometry. Computer Vision and Image Understanding, 78(1): 138--156, Apr. 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. T. Tuytelaars and K. Mikolajczyk. Local invariant feature detectors: A survey. Foundations and Trends in Computer Graphics and Vision, 3(3): 177--280, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Evaluation of feature detectors for registering aerial images

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Other conferences
          IVCNZ '12: Proceedings of the 27th Conference on Image and Vision Computing New Zealand
          November 2012
          547 pages
          ISBN:9781450314732
          DOI:10.1145/2425836

          Copyright © 2012 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 26 November 2012

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • poster

          Acceptance Rates

          Overall Acceptance Rate55of74submissions,74%

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader