Derivative-based scale invariant image feature detector with error resilience

IEEE Transactions on Image Processing

Volumn 23, Issue 5, 2014, Pages 2380-2391

  Mainali, Pradip ^a,b   Lafruit, Gauthier ^b   Tack, Klaas ^b   Van Gool, Luc ^a,c   Lauwereins, Rudy ^a,b  

^a Katholieke Universiteit Leuven   (Belgium)

^b IMEC   (Belgium)

^c ETH ZURICH   (Switzerland)

Author keywords

Gaussian derivatives; keypoint; registration; scale space; scaleinvariant features

Indexed keywords

FEATURE EXTRACTION; GAUSSIAN DISTRIBUTION; IMPULSE RESPONSE; OPTICAL SYSTEMS; OPTIMIZATION; SPECTROSCOPY;

GAUSSIAN DERIVATIVES; KEYPOINT; REGISTRATION; SCALE SPACES; SCALE-INVARIANT;

DETECTORS;

EID: 84899844175     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2014.2315959     Document Type: Article

References (30)

1. D. G. Lowe, "Distinctive image features from scale-invariant keypoints," Int. J. Comput. Vis., vol. 60, no. 2, pp. 91-110, 2004
2. H. Bay, E. Andreas, T. Tuytelaars, and L. V. Gool, "Speeded-up robust features (SURF)," Comput. Vis. Image Understand., vol. 110, no. 3, pp. 346-359, 2008
3. K. Mikolajczyk and C. Schmid, "Scale and affine invariant interest point detectors," Int. J. Comput. Vis., vol. 60, no. 1, pp. 63-86, 2004
4. T. Lindeberg, "Feature detection with automatic scale selection," Int. J. Comput. Vis., vol. 30, no. 2, pp. 79-116, 1998
5. P. Shippert, "Why use hyperspectral imagery," Photogram. Eng. Remote Sensing, vol. 70, no. 4, pp. 377-380, 2004
6. D. N. Stratis, K. L. Eland, J. C. Carter, S. J. Tomlinson, and S. M. Angel, "Comparison of acousto-optic and liquid crystal tunable filters for laserinduced breakdown spectroscopy," Appl. Spectrosc., vol. 55, no. 8, pp. 999-1004, Aug. 2001
7. G. T. Elerding, J. G. Thunen, and L. M. Woody, "Wedge imaging spectrometer: Application to drug and pollution law enforcement," Proc. SPIE, vol. 1479, pp. 380-392, Aug. 1991
8. N. Tack, A. Lambrechts, P. Soussana, and L. Haspeslagh, "A compact, high-speed and low-cost hyperspectral imager," Proc. SPIE, vol. 8266, pp. 82660Q-1-82660Q-13, Feb. 2012
9. S. Mallat, A Wavelet Tour of Signal Processing, 3rd ed. New York, NY, USA: Academic, 2008
10. M. Vetterli and J. Kovacevic, Wavelets and Subband Coding. Englewood Cliffs, NJ, USA: Prentice-Hall, 1995
11. H. Aanæs, A. Dahl, and K. S. Pedersen, "Interesting interest points: A comparative study of interest point performance on a unique data set," Int. J. Comput. Vis., vol. 97, no. 1, pp. 1-18, 2011
12. X. Gao, F. Sattar, and R. Venkateswarlu, "Multiscale corner detection of gray level images based on log-Gabor wavelet transform," IEEE Trans. Circuits Syst. Video Technol., vol. 17, no. 7, pp. 868-875, Jul. 2007
13. P. Bendale, B. Triggs, and N. Kingsbury, "Multiscale keypoint analysis based on complex wavelets," in Proc. Brit. Mach. Vis. Conf., 2010, pp. 49-1-49-10
14. S. M. Smith and J. M. Brady, "SUSAN-A new approach to low level image processing," Int. J. Comput. Vis., vol. 23, no. 1, pp. 45-78, 1997
15. E. Rosten, R. Porter, and T. Drummond, "Faster and better: A machine learning approach to corner detection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 1, pp. 105-119, Jan. 2010
16. J. Matas, O. Chum, U. Martin, and T. Pajdla, "Robust wide baseline stereo from maximally stable extremal regions," in Proc. Brit. Mach. Vis. Conf., vol. 1. 2002, pp. 384-393
17. E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, "ORB: An efficient alternative to SIFT or SURF," in Proc. IEEE ICCV, Nov. 2011, pp. 2564-2571
18. T. Kadir, A. Zisserman, and J. M. Brady, "An affine invariant salient region detector," in Proc. 8th ECCV, 2004, pp. 228-241
19. C. Harris and M. Stephens, "A combined corner and edge detection," in Proc. 4th Alvey Vis. Conf., 1988, pp. 147-151
20. C. Tomasi and T. Kanade, "Detection and tracking of point features," School Comput. Sci., Carnegie Mellon Univ., Pittsburgh, PA, USA, Tech. Rep. CMU-CS-91-132, Apr. 1991
21. P. Mainali, Q. Yang, G. Lafruit, L. V. Gool, and R. Lauwereins, "Robust low complexity corner detector," IEEE Trans. Circuits Syst. Video Technol., vol. 21, no. 4, pp. 87-127, Apr. 2010
22. E. Tola, V. Lepetit, and P. Fua, "A fast local descriptor for dense matching," in Proc. IEEE Conf. CVPR, Jun. 2008, pp. 1-8
23. A. Alahi, R. Ortiz, and P. Vandergheynst, "FREAK: Fast retina keypoint," in Proc. IEEE Conf. CVPR, Jun. 2012, pp. 510-517
24. M. Abramowitz and I. A. Stegun, Handbook of Mathematical Functions With Formulas, Graphs, and Mathematical Tables. New York, NY, USA: Dover, 1964
25. T. Lindeberg, "Scale-space for discrete signals," IEEE Trans. Pattern Anal. Mach. Intell., vol. 12, no. 3, pp. 234-254, Mar. 1990
26. R. Deriche, "Recursively implementing the Gaussian and its derivatives," INRIA, Villeneuve-d'Ascq, France, Tech. Rep. N 1893, 1993
27. K. Mikolajczyk. (2007). Oxford Data Set, Accessed on 2012 [Online]. Available: http://www.robots.ox.ac.uk/?vgg/research/affine
28. A. Vedaldi. (2011). Open Source SIFT Implementation, Accessed on 2012 [Online]. Available: http://www.vlfeat.org/?vedaldi/code/siftpp.html
29. H. Bay. (2011). SURF Implementation, Accessed on 2012 [Online]. Available: http://www.vision.ee.ethz.ch/?surf/
30. R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision. Cambridge, U.K.: Cambridge Univ. Press, 2000, pp. 87-127.