OSA's Digital Library

Journal of Optical Technology

Journal of Optical Technology

| SIMULTANEOUS RUSSIAN-ENGLISH PUBLICATION

  • Vol. 81, Iss. 6 — Jun. 1, 2014
  • pp: 327–333

Correlating images of three-dimensional scenes by clusterizing the correlated local attributes, using the Hough transform

R. O. Malashin  »View Author Affiliations


Journal of Optical Technology, Vol. 81, Issue 6, pp. 327-333 (2014)
http://dx.doi.org/10.1364/JOT.81.000327


View Full Text Article

Acrobat PDF (704 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

This paper describes algorithms for correlating images of arbitrary three-dimensional scenes by clusterizing correlated key points, using the Hough transform. The method is based on the well-known method of detecting objects, but an alternative approach is proposed for verifying clusters of correlated key points. Experimental results are given for different types of key points, confirming that the proposed method has a significant advantage over the use of the fundamental matrix.

© 2014 Optical Society of America

OCIS Codes
(100.5760) Image processing : Rotation-invariant pattern recognition
(100.3008) Image processing : Image recognition, algorithms and filters

History
Original Manuscript: March 5, 2014
Published: June 19, 2014

Citation
R. O. Malashin, "Correlating images of three-dimensional scenes by clusterizing the correlated local attributes, using the Hough transform," J. Opt. Technol. 81, 327-333 (2014)
http://www.opticsinfobase.org/jot/abstract.cfm?URI=jot-81-6-327


Sort:  Author  |  Year  |  Journal  |  Reset

References

  1. A.  Loui, M.  Das, “Matching of complex scenes based on constrained clustering,” in AAAI Fall Symposium: Multimedia Information Extraction, vol. FS-08-05, (2008), pp. 28–30.
  2. V.  Lutsiv, A.  Potapov, T.  Novikova, N.  Lapina, “Hierarchical 3D structural matching in the aerospace photographs and indoor scenes,” Proc. SPIE 5807, 455 (2005).
  3. M. V.  Peterson, “Clustering of a set of identified points on images of dynamic scenes, based on the principle of minimum description length,” J. Opt. Technol. 77, 701 (2010).
  4. A. S.  Potapov, I. A.  Malyshev, A. E.  Puysha, A. N.  Averkin, “New paradigm of learnable computer vision algorithms based on the representational MDL principle,” Proc. SPIE 7696, 769606 (2010).
  5. D. H.  Ballard, “Generalizing the Hough transform to detect arbitrary shapes,” Pattern Recogn. 13, 111 (1981).
  6. D. G.  Lowe, “Object recognition from local scale-invariant features,” in The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, Kerkyra, Greece, September20–27, 1999, pp. 1150–1157.
  7. H.  Bay, T.  Tuytelaars, L.  Van Gool, “SURF: Speeded Up Robust Features,” in Proceedings of the Ninth European Conference on Computer Vision, Graz, Austria, May7–13, 2006, pp. 404–417.
  8. D.  Lowe, “Local feature view clustering for 3D object recognition,” in IEEE Conference on Computer Vision and Pattern Recognition, Kauai, Hawaii, USA, December2001, pp. 682–688.
  9. R.  Raguram, J. M.  Frahm, M.  Pollefeys, “A comparative analysis of ransac techniques leading to adaptive real-time random sample consensus,” in Proceedings of the European Conference on Computer Vision, Marseille, France, October12–18, 2008, pp. 500–513.
  10. ERSP 3.1. Robotic Development Platform, http://www.mobile-vision-technologies.eu/archiv/download/MVT_ersp.pdf .
  11. S.  Leutenegger, M.  Chli, R.  Siegwart, “BRISK: Binary Robust Invariant Scalable Keypoints,” in Proceedings of the International Conference on Computer Vision, Barcelona, Spain, November8–11, 2011, pp. 2548–2555.

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited