Skip to main content
SpringerLink
Log in
Book cover

International Conference Image Analysis and Recognition

ICIAR 2011: Image Analysis and Recognition pp 221–231Cite as

Using Grid Based Feature Localization for Fast Image Matching

  • Conference paper

  • 1025 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6753))

Abstract

This paper presents a new model fitting approach to classify tentative feature matches as inliers or outliers during wide baseline image matching. The results show this approach increases the efficiency over traditional approaches (e.g. RANSAC) and other recently published approaches. During wide baseline image matching a feature matching algorithm generates a set of tentative matches. Our approach then classifies matches as inliers or outliers by determining if the matches are consistent with an affine model. In image pairs related by an affine transformation the ratios of areas of corresponding shapes is invariant. Our approach uses this invariant by sampling matches in a local region. Triangles are then formed from the matches and the ratios of areas of corresponding triangles are computed. If the resulting ratios of areas are consistent, then the sampled matches are classified as inliers. The resulting reduced inlier set is then processed through a model fitting step to generate the final set of inliers. In this paper we present experimental results comparing our approach to traditional model fitting and other affine based approaches. The results show the new method maintains the accuracy of other approaches while significantly increasing the efficiency of wide baseline matching for planar scenes.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. PAMI 27(10), 1615–1630 (2005)

    Article  Google Scholar 

  2. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  3. Rousseeuw, P., Leroy, A.: Robust Regression and Outlier Detection. Wiley, Chichester (1987)

    Book  MATH  Google Scholar 

  4. Chum, O., Matas, J.: Randomized ransac with td,d test. In: Proceedings of the 13th British Machine Vision Conference (BMVC), pp. 448–457 (2002)

    Google Scholar 

  5. Nister, D.: Preemptive ransac for live structure and motion estimation. MVA 16(5), 321–329 (2005)

    Google Scholar 

  6. Chum, O., Matas, J., Kittler, J.: Locally optimized RANSAC. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 236–243. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  7. Torr, P.H.S., Zisserman, A.: Mlesac: a new robust estimator with application to estimating image geometry. Comput. Vis. Image Underst. 78(1), 138–156 (2000)

    Article  Google Scholar 

  8. Tordoff, B., Murray, D.: Guided sampling and consensus for motion estimation. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 82–96. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  9. Wang, H.: Robust adaptive-scale parametric model estimation for computer vision. IEEE Trans. Pattern Anal. Mach. Intell. 26(11), 1459–1474 (2004), Senior Member-Suter, David

    Article  Google Scholar 

  10. Chum, O., Matas, J.: Optimal randomized ransac. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(8), 1472–1482 (2008)

    Article  Google Scholar 

  11. Wald, A.: Sequential Analysis. Dover, New York (1947)

    MATH  Google Scholar 

  12. Tordoff, B.J., Murray, D.W.: Guided-mlesac: Faster image transform estimation by using matching priors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1523–1535 (2005)

    Article  Google Scholar 

  13. Myatt, D.R., Torr, P.H.S., Nasuto, S.J., Bishop, J.M., Craddock, R.: Napsac: high noise, high dimensional robust estimation. In: BMVC 2002, pp. 458–467 (2002)

    Google Scholar 

  14. Torr, P.: Bayesian model estimation and selection for epipolar geometry and generic manifold fitting. IJCV 50(1), 35–61 (2002)

    Article  MATH  Google Scholar 

  15. Zhang, L., Rastgar, H., Wang, D., Vincent, A.: Maximum likelihood estimation sample consensus with validation of individual correspondences. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Kuno, Y., Wang, J., Wang, J.-X., Wang, J., Pajarola, R., Lindstrom, P., Hinkenjann, A., Encarnação, M.L., Silva, C.T., Coming, D. (eds.) ISVC 2009. LNCS, vol. 5875, pp. 447–456. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  16. Torr, P.: Philip torr’s home page, http://cms.brookes.ac.uk/staff/PhilipTorr/

  17. Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004)

    Book  MATH  Google Scholar 

  18. Fleck, D., Duric, Z.: Affine invariant-based classification of inliers and outliers for image matching. In: Kamel, M., Campilho, A. (eds.) ICIAR 2009. LNCS, vol. 5627, pp. 268–277. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  19. Fleck, D., Duric, Z.: An evaluation of affine invariant-based classification for image matching. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Kuno, Y., Wang, J., Pajarola, R., Lindstrom, P., Hinkenjann, A., Encarnação, M.L., Silva, C.T., Coming, D. (eds.) ISVC 2009. LNCS, vol. 5876, pp. 417–429. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  20. Fleck, D., Duric, Z.: Using local affine invariants to improve image matching. In: International Conference on Pattern Recognition, pp. 1844–1847 (2010)

    Google Scholar 

  21. Barber, C.B., Dobkin, D.P., Huhdanpaa, H.: The quickhull algorithm for convex hulls. ACM Transactions on Mathematical Software 22(4), 469–483 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  22. Leach, G.: Improving worst-case optimal delaunay triangulation algorithms. In: 4th Canadian Conference on Computational Geometry, p. 15 (1992)

    Google Scholar 

  23. Griesser, A.: Zurich building database, http://www.vision.ee.ethz.ch/showroom/zubud/

  24. Kovesi, P.D.: MATLAB and Octave functions for computer vision and image processing. School of Computer Science & Software Engineering, The University of Western Australia, http://www.csse.uwa.edu.au/~pk/research/matlabfns/

  25. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)

    Article  Google Scholar 

  26. Ma, Y., Soatto, S., Kosecka, J., Sastry, S.S.: An Invitation to 3-D Vision: From Images to Geometric Models. Springer, Heidelberg (2003)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, American University, Washington DC, 20016, USA

    Daniel Fleck & Zoran Duric

Authors
  1. Daniel Fleck
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zoran Duric
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Waterloo, N2L 3G1, Waterloo, ON, Canada

    Mohamed Kamel

  2. Faculty of Engineering, Institute of Biomedical Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Aurélio Campilho

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Fleck, D., Duric, Z. (2011). Using Grid Based Feature Localization for Fast Image Matching. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2011. Lecture Notes in Computer Science, vol 6753. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21593-3_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-21593-3_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21592-6

  • Online ISBN: 978-3-642-21593-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation