Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

Hessian-Based Affine Adaptation of Salient Local Image Features

  • Published:
Journal of Mathematical Imaging and Vision Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Affine covariant local image features are a powerful tool for many applications, including matching and calibrating wide baseline images. Local feature extractors that use a saliency map to locate features require adaptation processes in order to extract affine covariant features. The most effective extractors make use of the second moment matrix (SMM) to iteratively estimate the affine shape of local image regions. This paper shows that the Hessian matrix can be used to estimate local affine shape in a similar fashion to the SMM. The Hessian matrix requires significantly less computation effort than the SMM, allowing more efficient affine adaptation. Experimental results indicate that using the Hessian matrix in conjunction with a feature extractor that selects features in regions with high second order gradients delivers equivalent quality correspondences in less than 17% of the processing time, compared to the same extractor using the SMM.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, New York (2003)

    Google Scholar 

  2. Pollefeys, M., Van Gool, L., Vergauwen, M., Verbiest, F., Cornelis, K., Tops, J., Koch, R.: Visual modeling with a hand-held camera. Int. J. Comput. Vis. 59(3), 207–232 (2004)

    Article  Google Scholar 

  3. Furukawa, Y., Ponce, J.: Accurate, dense, and robust multi-view stereopsis. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)

    Google Scholar 

  4. Brown, M., Lowe, D.G.: Automatic panoramic image stitching using invariant features. Int. J. Comput. Vis. 74(1), 59–73 (2007)

    Article  Google Scholar 

  5. Carbonetto, P., Dorkó, G., Schmid, C., Kück, H., de Freitas, N.: Learning to recognize objects with little supervision. Int. J. Comput. Vis. 77(1), 219–237 (2008)

    Article  Google Scholar 

  6. Snavely, N., Seitz, S., Szeliski, R.: Modeling the world from Internet photo collections. Int. J. Comput. Vis. 80(2), 189–210 (2008)

    Article  Google Scholar 

  7. Fookes, C., Denman, S., Lakemond, R., Ryan, D., Sridharan, S., Piccardi, M.: Semi-supervised intelligent surveillance system for secure environments. In: IEEE International Symposium on Industrial Electronics (ISIE), pp. 2815–2820 (2010)

    Google Scholar 

  8. Tuytelaars, T., Mikolajczyk, K.: Local invariant feature detectors: a survey. Found. Trends Comput. Graph. Vis. 3(3), 177–280 (2008)

    Article  Google Scholar 

  9. Kadir, T., Zisserman, A., Brady, J.M.: An affine invariant salient region detector. In: Proc. European Conference on Computer Vision, pp. 228–241 (2004)

    Google Scholar 

  10. Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Van Gool, L.: A comparison of affine region detectors. Int. J. Comput. Vis. 65(1-2), 43–72 (2005)

    Article  Google Scholar 

  11. Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: Proc. British Machine Vision Conference, vol. 1, pp. 384–393 (2002)

    Google Scholar 

  12. Moreels, P., Perona, P.: Evaluation of features detectors and descriptors based on 3d objects. Int. J. Comput. Vis. 73(3), 263–284 (2007)

    Article  Google Scholar 

  13. Bay, H., Tuytelaars, T., Van Gool, L.: Surf: Speeded up robust features. In: Proc. European Conference on Computer Vision, pp. 404–417 (2006)

    Google Scholar 

  14. Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: Proc. European Conference on Computer Vision. Lecture Notes in Computer Science, vol. 3951, pp. 430–443. Springer, Berlin (2006)

    Google Scholar 

  15. Harris, C., Stephens, M.: A combined corner and edge detector. In: Proc. Alvey Vision Conference, pp. 189–192 (1988)

    Google Scholar 

  16. Beaudet, P.R.: Rotationally invariant image operators. In: Proc. International Joint Conference on Pattern Recognition, Kyoto, Japan, pp. 579–583 (1978)

    Google Scholar 

  17. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  18. Lindeberg, T.: Scale-Space Theory in Computer Vision, 1st edn. Kluwer Academic, Boston (1994)

    Google Scholar 

  19. Lindeberg, T.: Feature detection with automatic scale selection. Int. J. Comput. Vis. 30(2), 77–116 (1998)

    Google Scholar 

  20. Lowe, D.G.: Object recognition from local scale-invariant features. In: Proc. International Conference on Computer Vision, Corfu, Greece, pp. 1150–1157 (1999)

    Chapter  Google Scholar 

  21. Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. Int. J. Comput. Vis. 60, 63–86 (2004)

    Article  Google Scholar 

  22. Lakemond, R., McKinnon, D.N.R., Fookes, C., Sridharan, S.: A feature clustering algorithm for scale-space analysis of image structures. In: Proc. International Conference on Signal Processing and Communication Systems, pp. 186–192 (2007)

    Google Scholar 

  23. Lakemond, R.: Multiple camera management using wide baseline matching. Thesis (2010)

  24. Lindeberg, T., Gårding, J.: Shape-adapted smoothing in estimation of 3-d depth cues from affine distortions of local 2-d brightness structure. In: Proc. European Conference on Computer Vision. Lecture Notes in Computer Science, vol. 800, pp. 389–400. Springer, Berlin (1994)

    Google Scholar 

  25. Baumberg, A.: Reliable feature matching across widely separated views. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 774–781 (2000)

    Chapter  Google Scholar 

  26. Tuytelaars, T., Van Gool, L., D’haene, L., Koch, R.: Matching of affinely invariant regions for visual servoing. In: Proc. IEEE International Conference on Robotics and Automation, vol. 2, pp. 1601–1606 (1999)

    Google Scholar 

  27. Tuytelaars, T., Van Gool, L.: Wide baseline stereo matching based on local, affinely invariant regions. In: Proc. British Machine Vision Conference, Bristol, pp. 412–425 (2000)

    Google Scholar 

  28. van Gool, L., Tuytelaars, T., Turina, A.: Local features for image retrieval. In: Veltkamp, R.C., Burkhardt, H., Kriegel, H.-P. (eds.) State-Of-The-Art in Content-Based Image and Video Retrieval, pp. 21–41. Kluwer Academic, Dordrecht (2001)

    Google Scholar 

  29. Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)

    Article  Google Scholar 

  30. Lakemond, R., Fookes, C., Sridharan, S.: Affine adaptation of local image features using the Hessian matrix. In: Proc. IEEE conference on Advanced Video and Signal Based Surveillance, Genoa, Italy, pp. 496–501 (2009)

    Google Scholar 

  31. Yuen, K.: Bayesian Methods for Structural Dynamics and Civil Engineering. Wiley, Singapore (2010)

    Book  Google Scholar 

  32. Deriche, R.: Recursively implementing the Gaussian and its derivatives. Technical report, INRIA (April 1993)

  33. Young, I.T., van Vliet, L.J.: Recursive implementation of the Gaussian filter. Signal Process. 44(2), 139–151 (1995)

    Article  Google Scholar 

  34. Lakemond, R., Fookes, C., Sridharan, S.: Dense correspondence extraction in difficult uncalibrated scenarios. In: Proc. Digital Image Computing: Techniques and Applications, Melbourne, Australia, pp. 53–60. IEEE Computer Society Conference Publishing Services, New York (2009)

    Chapter  Google Scholar 

  35. 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 

  36. Longuet-Higgins, H.C.: A computer algorithm for reconstructing a scene from two projections. In: Readings in Computer Vision: Issues, Problems, Principles, and Paradigms, p. 61. Morgan Kaufmann, San Francisco (1987)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Image and Video Research Laboratory, Queensland University of Technology, GPO Box 2434, 2 George St, Brisbane, Queensland, 4001, Australia

    Ruan Lakemond, Sridha Sridharan & Clinton Fookes

Authors
  1. Ruan Lakemond
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sridha Sridharan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Clinton Fookes
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ruan Lakemond.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lakemond, R., Sridharan, S. & Fookes, C. Hessian-Based Affine Adaptation of Salient Local Image Features. J Math Imaging Vis 44, 150–167 (2012). https://doi.org/10.1007/s10851-011-0317-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10851-011-0317-8

Keywords

Search

Navigation