IMPROVING SIFT FOR IMAGE FEATURE EXTRACTION

Authors

  • Renata DEAK Faculty of Mathematics and Computer Science, Babeș-Bolyai University, Cluj-Napoca, Romania. Email: drhp0888@scs.ubbcluj.ro
  • Adrian STERCA Faculty of Mathematics and Computer Science, Babeș-Bolyai University, Cluj-Napoca, Romania. Email: forest@cs.ubbcluj.ro https://orcid.org/0000-0002-5911-0269
  • Ioan BĂDĂRÎNZĂ Faculty of Mathematics and Computer Science, Babeș-Bolyai University, Cluj-Napoca, Romania. Email: ionutb@cs.ubbcluj.ro

DOI:

https://doi.org/10.24193/subbi.2017.2.02

Keywords:

image feature extraction, SIFT, FAST.

Abstract

This paper reviews a classical image feature extraction algorithm, namely SIFT (i.e. Scale Invariant Feature Transform) and modifies it in order to increase its repeatability score. We are using an approach that is inspired from another computer vision algorithm, namely FAST. The tests presented in the evaluation section show that our approach (i.e. SIFT-FAST) obtains better repeatability scores over classical SIFT.

Author Biographies

Renata DEAK, Faculty of Mathematics and Computer Science, Babeș-Bolyai University, Cluj-Napoca, Romania. Email: drhp0888@scs.ubbcluj.ro

Faculty of Mathematics and Computer Science, Babeș-Bolyai University. 1 Mihail Kogălniceanu, RO-400084 Cluj-Napoca, Romania. Email: drhp0888@scs.ubbcluj.ro

Adrian STERCA, Faculty of Mathematics and Computer Science, Babeș-Bolyai University, Cluj-Napoca, Romania. Email: forest@cs.ubbcluj.ro

Faculty of Mathematics and Computer Science, Babeș-Bolyai University. 1 Mihail Kogălniceanu, RO-400084 Cluj-Napoca, Romania. Email: forest@cs.ubbcluj.ro

Ioan BĂDĂRÎNZĂ, Faculty of Mathematics and Computer Science, Babeș-Bolyai University, Cluj-Napoca, Romania. Email: ionutb@cs.ubbcluj.ro

Faculty of Mathematics and Computer Science, Babeș-Bolyai University. 1 Mihail Kogălniceanu, RO-400084 Cluj-Napoca, Romania. Email: ionutb@cs.ubbcluj.ro

References

Hassner, T., Mayzels, V., Zelnik-Manor, L., On SIFTs and their scales, IEEE Conference on Computer Vision and Pattern Recognition, Washington DC, USA, June 16-21, 2012, pp. 1522-1528.

Lowe, D., Object recognition from local scale-invariant features, In Proceedings of the 7th International Conference on Computer Vision, Washington DC, USA, September 20-25, 1999, pp. 1150-1157.

Rosten, E., Drummond, T.: Fusing points and lines for high performance tracking, 10th IEEE International Conference on Computer Vision, Washington DC, USA, October 17-20, 2005, pp. 1508-1515.

Otero, I.R., Delbracio, M., The anatomy of the SIFT method, Image Processing On Line, vol. 4, 2014, pp. 370-396.

Harris, C., Stephens, M.: A combined corner and edge detector, Proceedings of the 4th Alvey Vision Conference, Manchester, 31 August - 2 September, 1988, pp. 147-151.

Lindeberg, T., Scale-space theory in computer vision, Kluwer Academic Publishers Norwell, MA, USA,1994.

Florack, L.M.J., Haar Romeny, B.M.T., Koenderink, J.J., Viergever, M.A.: General intensity transformations and differential invariants, Journal of Mathematical Imaging and Vision, May 1994, Volume 4, Issue 2, pp 171-187.

Bay, H., Tuytelaars, T., Van Gool, L., Surf: Speeded up robust features., Proceedings of the European Conference on Computer Vision, Graz, Austria, May 2006, pp 404-417.

Baumberg, A., Reliable feature matching across widely separated views, Conference on Computer Vision and Pattern Recognition, Hilton Head Island, South Carolina, 15 June 2000, pp. 774-781.

Schaffalitzky, F., Zisserman, A., Multi-view matching for unordered image sets, or “How do I organize my holiday snaps’, European Conference on Computer Vision, Copenhagen, Denmark, May 28-31, 2002, pp. 414-431.

G. Yu and J-M. Morel, ASIFT: An Algorithm for Fully Affine Invariant Comparison, Image Processing On Line, vol. 1, 2011, pp. 438-469.

Rublee, E., Rabaud, V., Konolige, K., Bradski, G., ORB: An efficient alternative to SIFT or SURF, Proceedings of IEEE International Conference on Computer Vision, Washington DC, USA, November 06-13, 2011, pp. 2564-2571.

Se, S., Ng, H., Jasiobedzki, P., Moyung, T., Vision based modeling and localization for planetary exploration rovers, Proceedings of the 55th International Astronautical Congress, Vancouver, Canada, 4-8 October pp. 364-375.

Mikolajczyk, K., Schmid, C., A performance evaluation of local descriptors. Pattern Analysis and Machine Intelligence, vol. 27, issue 10, 2005, pp. 1615-1630.

Leutenegger, S., Chli, M., Siegwart, R.Y., BRISK: Binary Robust Invariant Scalable Keypoints, Proceedings of IEEE International Conference on Computer Vision, Barcelona, Spain, 6-13 November, 2011, pp. 2548-2555.

Rosten, E., Porter, R., Drummond, T., Faster and better: A machine learning approach to corner detection, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 32, issue 1, pp. 105-119, 2010.

Szeliski, R., Image alignment and stitching: a tutorial, Foundations and Trends in Computer Graphics and Computer Vision, Now Publishers, pp. 1-104, 2006.

Rosten E., Drummond, T., Machine learning for high-speed corner detection, European Conference on Computer Vision, Graz, Austria, May 07-13, 2006, pp. 430-443.

Ke, Y., Sukthankar, R., PCA-SIFT: A more distinctive representation for local image descriptors, IEEE Conference on Computer Vision and Pattern Recognition, Washington DC, USA, 27 June-2 July 2004.

Donald Hearn, M. Pauline Baker, Computer graphics, Prentice-Hall, USA, 1994.

Downloads

Published

2017-12-15

How to Cite

DEAK, R., STERCA, A., & BĂDĂRÎNZĂ, I. (2017). IMPROVING SIFT FOR IMAGE FEATURE EXTRACTION. Studia Universitatis Babeș-Bolyai Informatica, 62(2), 17–31. https://doi.org/10.24193/subbi.2017.2.02

Issue

Section

Articles