Adaptive and Fast Scale Invariant Feature Extraction

Emanuele Frontoni, Primo Zingaretti

Abstract

The Scale Invariant Feature Transform, SIFT, has been successfully applied to robot vision, object recognition, motion estimation, etc. Still, the parameter settings are not fully investigated, especially when dealing with variable lighting conditions. In this work, we propose a SIFT improvement that allows feature extraction and matching between images taken under different illumination. Also an interesting approach to reduce the SIFT computational time is presented. Finally, results of robot vision based localization experiments using the proposed approach are presented.

References

  1. T. Lindeberg, “Scale-space theory: A basic tool for analysing structures at different scales.” Journal of Applied Statistics, vol. 21, no. 2, pp. 224-270, 1994.
  2. K. Mikolajczyk and C. Schmid, “Indexing based on scale invariant interest points,” in Proceedings of International Conference on Computer Vision, July 2001, pp. 525-531.
  3. --, “A performance evaluation of local descriptors,” in Proceedings of Computer Vision and Pattern Recognition, June 2003.
  4. C. Schmid, R. Mohr, and C. Bauckhage, “Evaluation of interest point detectors.” International Journal of Computer Vision, vol. 37, no. 2, pp. 151-172, 2000.
  5. C. Harris and M. Stephens, “A combined corner and edge detector.” in Proceedings of the Fourth Alvey Vision Conference, Manchester, UK, 1988, pp. 148-151.
  6. D. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.
  7. S. Se, D. Lowe, and J. Little, “Vision-based mobile robot localization and mapping using scale-invariant features,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2001, Seoul, Korea, May 2001, pp. 2051-2058.
  8. E. Frontoni and P. Zingaretti, “Feature extraction under variable lighting conditions.” in Proceeding of CISI06 - Conferenza Italiana sui Sustemi Intelligenti, Ancona, Italy, September 2006.
  9. H. Tamimi, H. Andreasson, A. Treptow, T. Duckett, and A. Zell, “Localization of mobile robots with omnidirectional vision using particle filter and iterative sift,” in Proceeding of the 2nd European Conference on Mobile Robots, Ancona, Italy, September 2005, pp. 2-8.
Download


Paper Citation


in Harvard Style

Frontoni E. and Zingaretti P. (2007). Adaptive and Fast Scale Invariant Feature Extraction . In Robot Vision - Volume 1: Robot Vision, (VISAPP 2007) ISBN 978-972-8865-76-4, pages 117-125. DOI: 10.5220/0002066801170125


in Bibtex Style

@conference{robot vision07,
author={Emanuele Frontoni and Primo Zingaretti},
title={Adaptive and Fast Scale Invariant Feature Extraction},
booktitle={Robot Vision - Volume 1: Robot Vision, (VISAPP 2007)},
year={2007},
pages={117-125},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002066801170125},
isbn={978-972-8865-76-4},
}


in EndNote Style

TY - CONF
JO - Robot Vision - Volume 1: Robot Vision, (VISAPP 2007)
TI - Adaptive and Fast Scale Invariant Feature Extraction
SN - 978-972-8865-76-4
AU - Frontoni E.
AU - Zingaretti P.
PY - 2007
SP - 117
EP - 125
DO - 10.5220/0002066801170125