Distinctive image features from scale-invariant keypoints

International Journal of Computer Vision

Volumn 60, Issue 2, 2004, Pages 91-110

  Lowe, David G ^a  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

Image matching; Invariant features; Object recognition; Scale invariance

Indexed keywords

ALGORITHMS; HOUGH TRANSFORMS; IMAGE ANALYSIS; LEAST SQUARES APPROXIMATIONS; OBJECT RECOGNITION; PARAMETER ESTIMATION; PATTERN MATCHING; PROBABILITY; RADAR CLUTTER; REAL TIME SYSTEMS; ROBUSTNESS (CONTROL SYSTEMS);

CLUTTER AND OCCLUSION; IMAGE MATCHING; INVARIANT FEATURES; SCALE-INVARIANCE;

FEATURE EXTRACTION;

EID: 3042535216     PISSN: 09205691     EISSN: None     Source Type: Journal    
DOI: 10.1023/B:VISI.0000029664.99615.94     Document Type: Article

References (44)

1. Arya, S. and Mount, D.M. 1993. Approximate nearest neighbor queries in fixed dimensions. In Fourth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA'93), pp. 271-280.
2. Arya, S., Mount, D.M., Netanyahu, N.S., Silverman, R., and Wu, A.Y. 1998. An optimal algorithm for approximate nearest neighbor searching. Journal of the ACM, 45:891-923.
3. Ballard, D.H. 1981. Generalizing the Hough transform to detect arbitrary patterns. Pattern Recognition, 13(2):111-122.
4. Basri, R. and Jacobs, D.W. 1997. Recognition using region correspondences. International Journal of Computer Vision, 25(2): 145-166.
5. Baumberg, A. 2000. Reliable feature matching across widely separated views. In Conference on Computer Vision find Pattern Recognition, Hilton Head, South Carolina, pp. 774-781.
6. Beis, J. and Lowe, D.G. 1997. Shape indexing using approximate nearest-neighbour search in high-dimensional spaces. In Conference on Computer Vision and Pattern Recognition, Puerto Rico, pp. 1000-1006.
7. Brown, M. and Lowe, D.G. 2002. Invariant features from interest point groups. In British Machine Vision Conference, Cardiff, Wales, pp. 656-665.
8. Carneiro, G. and Jepson, A.D. 2002. Phase-based local features. In European Conference on Computer Vision (ECCV), Copenhagen, Denmark, pp. 282-296.
9. Crowley, J.L. and Parker, A.C. 1984. A representation for shape based on peaks and ridges in the difference of low-pass transform. IEEE Trans on Pattern Analysis and Machine Intelligence, 6(2): 156-170.
10. Edelman, S., Intrator, N., and Poggio, T. 1997. Complex cells and object recognition. Unpublished manuscript: http://kybele.psych.cornell.edu/~edelman/ archive. html
11. Fergus, R., Perona, P., and Zisserman, A. 2003. Object class recognition by unsupervised scale-invariant learning. In IEEE Conference on Computer Vision and Pattern Recognition, Madison, Wisconsin, pp. 264-271.
12. Friedman, J.H., Benuey, J.L., and Finkel, R.A. 1977. An algorithm for finding best matches in logarithmic expected time. ACM Transactions on Mathematical Software, 3(3):209-226.
13. Funt, B.V. and Finlayson, G.D. 1995. Color constant color indexing. IEEE Trans. on Pattern Analysis and Machine Intelligence. 17(5):522-529.
14. Grimson, E. 1990. Object Recognition by Computer: The Role of Geometric Constraints, The MIT Press: Cambridge, MA.
15. Harris, C. 1992. Geometry from visual motion. In Active Vision, A. Blake and A. Yuille (Eds.), MIT Press, pp. 263-284.
16. Harris, C. and Stephens, M. 1988. A combined corner and edge detector. In Fourth Alvey Vision Conference, Manchester, UK, pp. 147-151.
17. Hartley, R. and Zisserman, A. 2000. Multiple view geometry in computer vision, Cambridge University Press: Cambridge, UK.
18. Hough, P.V.C. 1962. Method and means for recognizing complex patterns. U.S. Patent 3069654.
19. Koenderink, J.J. 1984. The structure of images. Biological Cybernetics, 50:363-396.
20. Lindeberg, T. 1993. Detecting salient blob-like image structures and their scales with a scale-space primal sketch: A method for focus-of-attention. International Journal of Computer Vision, 11(3):283-318.
21. Lindeberg, T. 1994. Scale-space theory: A basic tool for analysing structures at different scales. Journal of Applied Statistics, 21(2):224-270.
22. Lowe, D.G. 1991. Fitting parameterized three-dimensional models to images. IEEE Trans. on Pattern Analysis and Machine Intelligence, 13(5):441-450.
23. Lowe, D.G. 1999. Object recognition from local scale-invariant features. In International Conference on Computer Vision, Corfu. Greece, pp. 1150-1157.
24. Lowe, D.G. 2001. Local feature view clustering for 3D object recognition. IEEE Conference on Computer Vision and Pattern Recognition, Kauai, Hawaii, pp. 682-688.
25. Luong, Q.T. and Faugeras, O.D. 1996. The fundamental matrix: Theory, algorithms, and stability analysis. International Journal of Computer Vision, 17(1):43-76.
26. Matas, J., Chum, O., Urban, M., and Pajdla, T. 2002. Robust wide baseline stereo from maximally stable extremal regions. in British Machine Vision Conference. Cardiff, Wales, pp. 3841-3843.
27. Mikolajezyk, K. 2002. Detection of local features invariant to affine transformations, Ph.D. thesis, Institut National Polytechnique de Grenoble, France.
28. Mikolajczyk, K. and Schmid, C. 2002. An affine invariant interest point detector. In European Conference on Computer Vision (ECCV), Copenhagen. Denmark, pp. 128-142.
29. Mikolajczyk, K., Zisserman, A., and Schmid, C. 2003. Shape recognition with edge-based features. In Proceedings of the British Machine Vision Conference, Norwich, U.K.
30. Moravec, H. 1981. Rover visual obstacle avoidance. In International Joint Conference on Artificial Intelligence, Vancouver, Canada, pp. 785-790.
31. Nelson, R.C. and Selinger, A. 1998. Large-scale tests of a keyed, appearance-based 3-D object recognition system. Vision Research, 38(15):2469-2488.
32. Pope, A.R. and Lowe, D.G. 2000. Probabilistic models of appearance for 3-D object recognition. International Journal of Computer Vision, 40(2):149-167.
33. Pritchard, D. and Heidrich, W. 2003. Cloth motion capture. Computer Graphics Forum (Eurographics 2003), 22(3):263-271.
34. Schaffalitzky, F. and Zisserman, A. 2002. Multi-view matching for unordered image sets, or 'How do I organize my holiday snaps?"' In European Conference on Computer Vision, Copenhagen, Denmark, pp. 414-431.
35. Schiele, B. and Crowley, J.L. 2000. Recognition without correspondence using multidimensional receptive field histograms. International Journal of Computer Vision, 36(1):31-50.
36. Schmid, C. and Mohr, R. 1997. Local grayvalue invariants for image retrieval. IEEE Trans. on Pattern Analysis and Machine Intelligence, 19(5):530-534.
37. Se, S., Lowe, D.G., and Little, J. 2001. Vision-based mobile robot localization and mapping using scale-invariant features. In International Conference on Robotics and Automation, Seoul, Korea, pp. 2051-2058.
38. Se, S., Lowe, D.G., and Little, J. 2002. Global localization using distinctive visual features. In International Conference on Intelligent Robots and Systems, IROS 2002, Lausanne, Switzerland, pp. 226-231.
39. Shokoufandeh, A., Marsic, I., and Dickinson, S.J. 1999. View-based object recognition using saliency maps. Image and Vision Computing, 17:445-460.
40. Torr, P. 1995. Motion segmentation and outlier detection, Ph.D. Thesis, Dept. of Engineering Science, University of Oxford, UK.
41. Tuytelaars, T. and Van Gool, L.2000. Wide baseline stereo based on local, affinely invariant regions. In British Machine Vision Conference, Bristol, UK pp. 412-422.
42. Weber, M., Welling, M., and Perona, P. 2000. Unsupervised learning of models for recognition. In European Conference on Computer Vision, Dublin, Ireland, pp. 18-32.
43. Witkin, A.P. 1983. Scale-space filtering. In International Joint Conference on Artificial Intelligence, Karlsruhe, Germany, pp. 1019-1022.
44. Zhang, Z., Deriche, R., Faugeras, O., and Luong, Q.T. 1995. A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. Artificial Intelligence. 78:87-119.