Fully affine invariant SURF for image matching

Neurocomputing

Volumn 85, Issue , 2012, Pages 6-10

  Pang, Yanwei ^a   Li, Wei ^a   Yuan, Yuan ^b   Pan, Jing ^c  

^a TIANJIN UNIVERSITY   (China)

^b XI AN INSTITUTE OF OPTICS AND PRECISION MECHANICS   (China)

^c TIANJIN UNIVERSITY OF TECHNOLOGY AND EDUCATION   (China)

Author keywords

FAIR SURF; Feature extraction; Image matching; SURF

Indexed keywords

AFFINE INVARIANT; COMPUTER VISION APPLICATIONS; FAIR-SURF; IMAGE FEATURES; SCALE INVARIANT FEATURE TRANSFORMS; SPEEDED UP ROBUST FEATURES; SURF;

ALGORITHMS; COMPUTER VISION; FEATURE EXTRACTION; IMAGE MATCHING;

AFFINE TRANSFORMS;

AFFINE SCALE INVARIANT FEATURE TRANSFORM; AFFINE SCALE INVARIANT FEATURE TRANSFORM ALGORITHM; ALGORITHM; ARTICLE; CONTROLLED STUDY; FEATURE EXTRACTION; IMAGE MATCHING; IMAGE PROCESSING; IMAGING SYSTEM; PRIORITY JOURNAL; SCALE INVARIANT FEATURE TRANSFORM ALGORITHM; SIMULATION; SPEEDED UP ROBUST FEATURES ALGORITHM;

EID: 84862827035     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.12.006     Document Type: Article

References (15)

1. Lin W., Oakes M.P., Tait J. Improving image annotation via representative feature vector selection. Neurocomputing 2010, 73(11-12):1774-1782.
2. Hu Q., Chen X., Zhang L., Yu D. Feature evaluation and selection based on neighborhood soft margin. Neurocomputing 2010, 73(10-12):2114-2124.
3. He X. Laplacian regularized d-optimal design for active learning and its application to image retrieval. IEEE Trans. Image Process. 2010, 19(1):254-263.
4. C. Harris, M. Stephens, A combined corner and edge detector, in: Proceedings of the Alvey Vision Conference, Manchester, 1988, pp. 147-152.
5. K. Mikolajczyk, C. Schmid, An affi{ligature}ne invariant interest point detector, in: Proceedings of the Seventh European Conference on Computer Vision, Copenhagen, Denmark, 2002, pp. 128-142.
6. Matas J., Chum O., Urban M., Pajdla T. Robust wide-baseline stereo from maximally stable extremal regions. Image Vision Comput. 2004, 22(10):761-767.
7. Lowe D.G. Distinctive image features from scale-invariant key points. Int. J. Comp. Vis. 2004, 60(2):91-110.
8. Morel J.M., Yu G. ASIFT: a new framework for fully affine invariant image comparison. SIAM J. Imaging Sci. 2009, 2(2):438-469.
9. Y. Ke, R. Sukthankar, PCA-SIFT: a more distinctive representation for local image descriptors, in: Proceedings of the Conference on Computer Vision and Pattern Recognition, Washington, DC, USA, 2004, pp. 506-513.
10. Mikolajczyk K., Schmid C. A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 2005, 27(10):1615-1630.
11. Bay H., Ess A., Tuytelaars T., Gool L.V. SURF: speeded-up robust features. Comp. Vision Image Understanding 2008, 110(3):346-359.
12. M. Agrawal, K. Konolige, M.R. Blas, Censure: center surround extremas for realtime feature detection and matching, in: Proceedings of the Tenth European Conference on Computer Vision, Prague, Czech Republic, 2008, pp. 102-115.
13. M. Ebrahimi, W.W. Mayol-Cuevas, SUSurE: speeded up surround extrema feature detector and descriptor for realtime applications, in: Proceedings of the Conference on Computer Vision and Pattern Recognition Workshops, Miami, Florida, USA, 2009, pp. 9-14.
14. Juan L., Gwun O. A comparison of SIFT, PCA-SIFT and SURF. Int. J. Image Process. 2009, 3(4):143-152.
15. Pang Y., Yuan Y., Li X., Pan J. Efficient HOG human detection. Signal Process. 2011, 91(4):773-781.