On SIFTs and their scales

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2012, Pages 1522-1528

  Hassner, Tal ^a   Mayzels, Viki ^b   Zelnik Manor, Lihi ^b  

^a OPEN UNIVERSITY OF ISRAEL   (Israel)

^b TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

DESCRIPTOR REPRESENTATION; DESCRIPTORS; DIFFERENT SCALE; FEATURE MATCHING; IMAGE PIXELS; LINEAR SUBSPACE; MULTIPLE SCALE; QUALITATIVE TEST; SCALE INVARIANT; SPARSE SET;

COMPUTER VISION; FEATURE EXTRACTION;

PIXELS;

EID: 84866670262     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2012.6247842     Document Type: Conference Paper

References (30)

1. S. Baker, D. Scharstein, J. Lewis, S. Roth, M. Black, and R. Szeliski. A database and evaluation methodology for optical flow. IJCV, 92(1):1-31, 2001.
2. C. Barnes, E. Shechtman, D. B. Goldman, and A. Finkelstein. The generalized PatchMatch correspondence algorithm. In ECCV, Sept. 2010.
3. R. Basri, T. Hassner, and L. Zelnik-Manor. Approximate nearest subspace search with applications to pattern recognition. In CVPR, June 2007.
4. R. Basri, T. Hassner, and L. Zelnik-Manor. A general framework for approximate nearest subspace search. In ICCVW, pages 109-116. IEEE, 2009.
5. R. Basri, T. Hassner, and L. Zelnik-Manor. Approximate nearest subspace search. TPAMI, 33(2):266-278, 2010.
6. H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool. Speeded-up robust features (surf). CVIU, 110(3):346-359, 2008.
7. T. Brox, C. Bregler, and J. Malik. Large displacement optical flow. In CVPR, pages 41-48, 2009.
8. A. Bruhn, J. Weickert, and C. Schnörr. Lucas/kanade meets horn/schunck: Combining local and global optic flow methods. IJCV, 61(3):211-231, 2005.
9. A. Edelman, T. Arias, and S. Smith. The geometry of algorithms with orthogonality constraints. SIAM Journal on Matrix Analysis and Applications, 20:303-353, 1998.
10. R. I. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, ISBN: 0521540518, second edition, 2004.
11. H. Hirschmüller and D. Scharstein. Evaluation of stereo matching costs on images with radiometric differences. TPAMI, pages 1582-1599, 2008.
12. I. Kokkinos and A. Yuille. Scale invariance without scale selection. In CVPR, pages 1-8, 2008.
13. T. Lindeberg. Scale-space theory: A basic tool for analysing structures at different scales,". J. of App. stat., 21(2):225-270, 1994.
14. T. Lindeberg. Feature detection with automatic scale selection. IJCV, 30(2):79-116, 1998.
15. T. Lindeberg. Principles for automatic scale selection. Handbook on Computer Vision and Applications, 2:239-274, 1999.
16. C. Liu, J. Yuen, and A. Torralba. Sift flow: Dense correspondence across scenes and its applications. TPAMI, 33(5):978-994, 2011.
17. C. Liu, J. Yuen, A. Torralba, J. Sivic, and W. Freeman. Sift flow: dense correspondence across different scenes. In ECCV, pages 28-42, 2008.
18. D. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91-110, 2004.
19. K. Mikolajczyk. Detection of local features invariant to affine transformations. PhD thesis, Institut National Polytechnique de Grenoble, France, 2002.
20. K. Mikolajczyk and C. Schmid. Scale & affine invariant interest point detectors. IJCV, 60(1):63-86, 2004.
21. K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors. TPAMI, 27(10):1615-1630, 2005.
22. J. Morel and G. Yu. Is sift scale invariant? Inverse Problems and Imaging (IPI), 5(1):115-136, 2011.
23. E. Nowak, F. Jurie, and B. Triggs. Sampling strategies for bag-of-features image classification. In ECCV, pages 490-503, 2006.
24. I. Simon and S. Seitz. A probabilistic model for object recognition, segmentation, and non-rigid correspondence. In CVPR, pages 1-7, 2007.
25. C. Strecha, T. Tuytelaars, and L. Gool. Dense matching of multiple wide-baseline views. In ICCV, 2003.
26. E. Tola, V. Lepetit, and P. Fua. Daisy: An efficient dense descriptor applied to wide-baseline stereo. TPAMI, pages 815-830, 2009.
27. M. Varma and R. Garg. Locally invariant fractal features for statistical texture classification. In ICCV, pages 1-8, 2007.
28. A. Vedaldi and B. Fulkerson. Vlfeat: An open and portable library of computer vision algorithms. In Proc. int. conf. on Multimedia, pages 1469-1472, 2010.
29. L. Wolf, T. Hassner, and I. Maoz. Face recognition in unconstrained videos with matched background similarity. In CVPR, pages 529-534, 2011.
30. J. Yao andW. Cham. 3D modeling and rendering from multiple wide-baseline images by match propagation. Signal processing. Image communication, 21(6):506-518, 2006.