Principal curvature-based region detector for object recognition

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

Volumn , Issue , 2007, Pages

  Deng, Hongli ^a   Zhang, Wei ^a   Mortensen, Eric ^a   Dietterich, Thomas ^a   Shapiro, Linda ^b  

^a OREGON STATE UNIVERSITY   (United States)

^b UNIVERSITY OF WASHINGTON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DETECTORS; IMAGE ANALYSIS; ROBUSTNESS (CONTROL SYSTEMS);

FEATURE MATCHING; PRINCIPAL CURVATURE-BASED REGIONS (PCBR);

OBJECT RECOGNITION;

EID: 35148887697     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2007.382972     Document Type: Conference Paper

References (27)

1. H. Asada and M. Brady. The curvature primal sketch. PAMI, 8(1):2-14, 1986. 2
2. A. Baumberg. Reliable feature matching across widely separated views. CVPR, pages 774-781, 2000. 2
3. P. Beaudet. Rotationally invariant image operators. ICPR, pages 579-583, 1978. 2
4. J. Canny. A computational approach to edge detection. PAMI, 8:679-698, 1986. 2
5. R. Deriche and G. Giraudon. A computational approach for corner and vertex detection. IJCV, 10(2): 101-124, 1992. 2
6. T. G. Dietterich. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation, 10(7): 1895-1924,1998. 6
7. C. Harris and M. Stephens. A combined corner and edge detector. Alvey Vision Conf., pages 147-151, 1988. 2, 3
8. F. Jurie and C. Schmid. Scale-invariant shape features for recognition of object categories. CVPR, 2:90-96, 2004. 1, 2
9. T. Kadir and M. Brady. Scale, saliency and image description. IJCV, 45(2):83-105, 2001. 2
10. N. Landwehr, M. Hall, and E. Frank. Logistic model trees. Machine Learning, 59(1-2):161-205, 2005. 6, 7
11. T. Lindeberg. Feature detection with automatic scale selection. IJCV, 30(2):79-116, 1998. 2
12. T. Lindeberg and J. Garding. Shape-adapted smoothing in estimation of 3-d shape cues from affine deformations of local 2-d brightness structure. Image and Vision Computing, pages 415-434, 1997. 2
13. D. G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91-110, 2004. 2, 3
14. G. Loy and J.-O. Eklundh. Detecting symmetry and symmetric constellations of features. ECCV, pages 508-521, 2006. 7
15. J. Matas, O. Chum, M. Urban, and T. Pajdla. Robust widebaseline stereo from maximally stable extremal regions. Image and Vision Computing, 22(10):761-767, 2004. 2
16. G. Medioni and Y. Yasumoto. Corner detection and curve representation using cubic b-splines. CVGIP, 39:267-278, 1987. 2
17. K. Mikolajczyk and C. Schmid. An affine invariant interest point detector. ECCV, 1(1): 128-142, 2002. 2
18. K. Mikolajczyk and C. Schmid. Scale and affine invariant interest point detectors. IJCV, 60(1):63-86, 2004. 2, 3
19. K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, and L. V. Gool. A comparison of affine region detectors. IJCV, 2005. 2, 3, 4, 5
20. F. Mokhtarian and R. Suomela. Robust image corner detection through curvature scale space. PAMI, 20(12): 1376-1381, 1998. 2
21. H. Moravec. Towards automatic visual obstacle avoidance. International Joint Conf. on Artificial Intelligence, page 584, 1977. 2
22. A. Opelt, M. Fussenegger, A. Pinz, and P. Auer. Weak hypotheses and boosting for generic object detection and recognition. ECCV, pages 71-84, 2004. 5, 6, 7
23. E. Shilat, M. Werman, and Y. Gdalyahu. Ridge's corner detection and correspondence. CVPR, pages 976-981, 1997. 2
24. S. Smith and J. M. Brady. Susan-a new approach to low level image processing. IJCV, 23(1):45-78, 1997. 2
25. C. Steger. An unbiased detector of curvilinear structures. PAMI, 20(2): 113-125, 1998. 1, 3
26. T. Tuytelaars and L. V. Gool. Wide baseline stereo matching based on local, affinely invariant regions. BMVC, pages 412-425, 2000. 2
27. T. Tuytelaars and L. V. Gool. Matching widely separated views based on affine invariant regions. IJCV, 59(1):61-85, 2004. 1, 2