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FESID: Finite Element Scale Invariant Detector

  • Dermot Kerr
  • Sonya Coleman
  • Bryan Scotney
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5716)

Abstract

Recently, finite element based methods have been used to develop gradient operators for edge detection that have improved angular accuracy over standard techniques. A more prominent issue in the field of image processing has become the use of interest point detectors and to this end we expand upon this research developing a finite element scale invariant interest point detector that is based on the same multi-scale approach used in the SURF detector. The operator differs in that the autocorrelation matrix is used to select the interest point location and the derivative and smoothing operations are combined into one operator developed through the use of the finite element framework.

Keywords

Interest Point Integral Image Multiscale Approach Interest Point Detector Scale Selection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: Speeded-Up Robust Features (SURF). CVIU 110(3), 346–359 (2008)Google Scholar
  2. 2.
    Mikolajczyk, K., Schmid, C.: Scale & Affine Invariant Interest Point Detectors. IJCV 60(1), 63–86 (2004)CrossRefGoogle Scholar
  3. 3.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of Alvey Vision Conference, vol. 15, pp. 147–151 (1988)Google Scholar
  4. 4.
    Dufournaud, Y., Schmid, C., Horaud, R.: Matching images with different resolutions. In: Proceedings of IEEE CVPR, vol. 1, pp. 612–618 (2000)Google Scholar
  5. 5.
    Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. IJCV 60(2), 91–110 (2004)CrossRefGoogle Scholar
  6. 6.
    Mikolajczyk, K., Schmid, C.: Indexing based on scale invariant interest points. In: Proceedings of ICCV, vol. 1, pp. 525–531 (2001)Google Scholar
  7. 7.
    Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., van Gool, L.: A Comparison of Affine Region Detectors. IJCV 65(1), 43–72 (2005)CrossRefGoogle Scholar
  8. 8.
    Coleman, S., Kerr, D., Scotney, B.: Concurrent Edge and Corner Detection. In: Proceedings of IEEE ICIP, pp. 273–276 (2007)Google Scholar
  9. 9.
    Kerr, D., Coleman, S., Scotney, B.: Near-Circular Corner and Edge Detection Operators. In: Proceedings of IEEE IMVIP, pp. 7–14 (2007)Google Scholar
  10. 10.
    Coleman, S., Scotney, B., Kerr, D.: Integrated edge and corner detection. In: Proceedings of ICIAP (2007)Google Scholar
  11. 11.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. CVPR 1, 511–518 (2001)Google Scholar
  12. 12.
    Scotney, B., Coleman, S.: Improving angular error via systematically designed near-circular Gaussian-based feature extraction operators. Pattern Recognition 40(5), 1451–1465 (2007)CrossRefzbMATHGoogle Scholar
  13. 13.
    Bay, H., Tuytelaars, T., Van Gool, L.: Surf: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  14. 14.
    Schmid, C., Mohr, R., Bauckhage, C.: Comparing and Evaluating Interest Points. In: Proceedings of ICCV, pp. 230–235. IEEE Computer Society Press, Los Alamitos (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Dermot Kerr
    • 1
  • Sonya Coleman
    • 1
  • Bryan Scotney
    • 2
  1. 1.School of Computing and Intelligent SystemsUniversity of UlsterMageeNorthern Ireland
  2. 2.School of Computing and Information EngineeringUniversity of UlsterColeraineNorthern Ireland

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