JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Recent Advances in Feature Detectors and Descriptors: A Survey
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Recent Advances in Feature Detectors and Descriptors: A Survey
Lee, Haeseong; Jeon, Semi; Yoon, Inhye; Paik, Joonki;
  PDF(new window)
 Abstract
Local feature extraction methods for images and videos are widely applied in the fields of image understanding and computer vision. However, robust features are detected differently when using the latest feature detectors and descriptors because of diverse image environments. This paper analyzes various feature extraction methods by summarizing algorithms, specifying properties, and comparing performance. We analyze eight feature extraction methods. The performance of feature extraction in various image environments is compared and evaluated. As a result, the feature detectors and descriptors can be used adaptively for image sequences captured under various image environments. Also, the evaluation of feature detectors and descriptors can be applied to driving assistance systems, closed circuit televisions (CCTVs), robot vision, etc.
 Keywords
Keypoints;Feature detection;Feature description;Image matching;Invariant features;Computational cost;
 Language
English
 Cited by
 References
1.
H. Moravec, "Obstacle avoidance and navigation in the real world by a seeing robot rover," Tech Report CMU-RI-TR-3, Carnegie-Mellon University, Robotics Institute, September 1980.

2.
C. Harris and M. Stephens, "A combined corner and edge detector," In Proc. Of Fourth Alvey Vision Conference, pp. 147-151, 1988.

3.
D. Lowe, "Distinctive image features from scaleinvariant keypoints," International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, November 2004. crossref(new window)

4.
H. Bay, T. Tuytelaars, and L. Gool, "SURF: Speeded up robust features," European Conference on Computer Vision, vol. 3951, pp. 404-417, May 2006.

5.
H. Bay, A. Ess, T. Tuytelaars, and L. Gool, "Speeded-up robust features (SURF)," Computer Vision and Understanding, vol. 110, no. 3, pp. 346-359, June 2008. crossref(new window)

6.
E. Rosten and T. Drummond, "Fusing points and lines for high performance tracking," Int. Conf. Computer Vision, vol. 2, pp. 17-21, October 2005.

7.
E. Rosten and T. Drummond, "Machine learning for high-speed corner detection," European Conference on Computer Vision, vol. 3951, pp. 430-443, May 2006.

8.
M. Calonder, V. Lepetit, C. Strecha, and P. Fua, "BRIEF: Binary Robust Independent Elementary Features," European Conference on Computer Vision, vol. 6314, pp. 778-792, September 2010.

9.
E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, "ORB: an efficient alternative to SIFT or SURF," Int. Conf. Computer Vision, pp. 2564-2571, November 2011.

10.
S. Leutenegger, M. Chli and R. Siegwart, "BRISK: Binary robust invariant scalable keypoints," Int. Conf. Computer Vision, pp. 2548-2555, November 2011.

11.
A. Alahi, R. Ortiz, and P. Vandergheynst, "FREAK: Fast retina keypoint," Int. Conf. Computer Vision and Pattern Recognition, pp. 510-517, June 2012.

12.
K. Mikolajczyk and C. Schmid, "A performance evaluation of local descriptors," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 10, pp. 1615-1630, October 2005. crossref(new window)

13.
K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, and L. Gool, "A comparison of affine region dtectors," International Journal of Computer Vision, vol. 65, no. 1, pp. 43-72, November 2005. crossref(new window)

14.
C. Strecha, A. Bronstein, M. Bronstein, and P. Fua, "LDAHash: Improved Matching with Smaller Descriptors," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 1, pp. 66-78, January 2012. crossref(new window)

15.
T. Trzcinski and V. Lepetit, "Efficient Discriminative Projections for Compact Binary Descriptors," European Conference on Computer Vision, vol. 7572, pp. 228-242, October 2012.

16.
X. Xu, L. Tian, J. Feng, and J. Zhou, "OSRI: A Rotationally Invariant Binary Descriptor," IEEE Transactions on Image Processing, vol. 23, no. 7, pp. 2983-2995, July 2014. crossref(new window)