Pattern Recognition and Image Analysis

, Volume 22, Issue 2, pp 380–385 | Cite as

A projection local image descriptor

  • D. V. SorokinEmail author
  • A. S. Krylov
Representation, Processing, Analysis, and Understanding of Images


This paper presents a projection local image descriptor. The projection local image descriptor construction is based on the local image expansion into the set of Gauss-Laguerre circular harmonic functions in the support region of a keypoint. The keypoints detection method is considered. It is also based on the analysis of image projections on the set of Gauss-Laguerre circular harmonic functions. The efficient technique for descriptors elements computation is introduced. The 2D Hermite projection method is used to accelerate the local image descriptors construction. The proposed approach is tested on the task of image points matching. The test results approved the ability of acceleration of descriptors construction process up to several times.


Gauss-Laguerre circular harmonic functions Hermite functions projection method image keypoints local image descriptor keypoints matching 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    F. Schaffalitzky and A. Zisserman, “Multi-View Matching for Unordered Image Sets,” in Proc. ECCV2002 (Copenhagen, 2008), pp. 414–431.Google Scholar
  2. 2.
    T. Tuytelaars, V. Ferrari, and L. Van Gool, “Simultaneous Object Recognition and Segmentation from Single or Multiple Model Views,” Int. J. Comput. Vision 67(2), 159–188 (2006).CrossRefGoogle Scholar
  3. 3.
    Cl. Morand, J. Benois-Pineau, J.-Ph. Domenger, J. Zepeda, E. Kijak, and Ch. Guillemot, “Scalable Object-Based Video Retrieval in HD Video Databases,” Image Commun. 25(6), 450–465 (2010).Google Scholar
  4. 4.
    C. G. Harris and M. Stephens, “A Combined Corner and Edge Detector,” in Proc. 4th Alvey Vision Conf. (Manchester, 1998), pp. 147–151.Google Scholar
  5. 5.
    D. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” Int. J. Comput. Vision 60(2), 91–110 (2004).CrossRefGoogle Scholar
  6. 6.
    L. Sorgi, N. Cimminiello, and A. Neri, “Keypoints Selection in the Gauss Laguerre Transformed Domain,” in Proc. BMVC06 (Edinburgh, 2006), pp. 133–142.Google Scholar
  7. 7.
    H. Hse and A.R. Newton, “Sketched Symbol Recognition Using Zernike Moments,” in Proc. ICPR04 (Cambridge, 2004), pp. 367–370.Google Scholar
  8. 8.
    K. Mikolajczyk and C. Schmid, “A Performance Evaluation of Local Descriptors”, IEEE Trans. PAMI 27(10), 1615–1630 (2004).CrossRefGoogle Scholar
  9. 9.
    G. Jacovitti and A. Neri, “Multiresolution Circular Harmonic Decomposition,” IEEE Trans. Signal Processing 48(11), 3242–3247 (2000).CrossRefMathSciNetGoogle Scholar
  10. 10.
    E. Zauderer, “Complex Argument Hermite-Gaussian and Laguerre-Gaussian Beams,” J. Opt. Soc. Amer. A 3(4), 465–469 (1986).CrossRefGoogle Scholar
  11. 11.
    E. D. Di Claudio, G. Jacovitti, and A. Laurenti, “Maximum Likelihood Orientation Estimation of 1D Patterns in Laguerre-Gauss Subspaces,” IEEE Trans. Image Processing 19(5), 1113–1125 (2010).CrossRefGoogle Scholar
  12. 12.
    A. Krylov and D. Korchagin, “Fast Hermite Projection Method,” Lecture Notes Comp. Sci. 4141, 329–338 (2006).CrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2012

Authors and Affiliations

  1. 1.Centre for Biomedical Image Analysis, Faculty of InformaticsMasaryk UniversityBrnoCzech Republic
  2. 2.Laboratory of Mathematical Methods of Image Processing, Faculty of Computational Mathematics and CyberneticsMoscow State UniversityMoscowRussia

Personalised recommendations