Evaluation of interest point detectors

International Journal of Computer Vision

Volumn 37, Issue 2, 2000, Pages 151-172

  Schmid, Cordelia ^a   Mohr, Roger ^a   Bauckhage, Christian ^a  

^a INRIA RHÔNE ALPES   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; FEATURE EXTRACTION; IMAGE RECONSTRUCTION; MATHEMATICAL MODELS; MATRIX ALGEBRA; OBJECT RECOGNITION; PATTERN MATCHING;

FEATURE DETECTORS; INFORMATION CONTENT; INTEREST POINTS; QUANTITATIVE EVALUATION;

COMPUTER VISION;

EID: 0034206859     PISSN: 09205691     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1008199403446     Document Type: Article

References (49)

1. Asada, H. and Brady, M. 1986. The curvature primal sketch. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(1):2-14.
2. Baker, S. and Nayar, S.K. 1999. Global measures of coherence for edge detector evaluation. In Proceedings of the Conference on Computer Vision and Pattern Recognition, Fort Collins, Colorado, USA, pp. 373-379.
3. Baker, S., Nayar, S.K., and Murase, H. 1998. Parametric feature detection. International Journal of Computer Vision, 27(1):27-50.
4. Beaudet, P.R. 1978. Rotationally invariant image operators. In Proceedings of the 4th International Joint Conference on Pattern Recognition, Tokyo, pp. 579-583.
5. Bowyer, K.W., Kranenburg, C., and Dougherty, S. 1999. Edge detector evaluation using empirical ROC curves. In Proceedings of the Conference on Computer Vision and Pattern Recognition, Fort Collins, Colorado, USA, pp. 354-359.
6. Brand, P. and Mohr, R. 1994. Accuracy in image measure. In Proceedings of the SPIE Conference on Videometrics III, Boston, Massachusetts, USA, Vol. 2350, pp. 218-228.
7. Canny, J. 1986. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6):679-698.
8. Coelho, C., Heller, A., Mundy, J.L., Forsyth, D., and Zisserman, A. 1991. An experimental evaluation of projective invariants. In Proceeding of the DARPA-ESPRIT Workshop on Applications of Invariants in Computer Vision, Reykjavik, Iceland, pp. 273-293.
9. Cooper, J., Venkatesh, S., and Kitchen, L. 1993. Early jump-out corner detectors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(8):823-833.
10. Cottier, J.C. 1994. Extraction et appariements robustes des points d'intérêt de deux images non étalonnées. Technical Report, LIFIA-IMAG-INRIA Rhône-Alpes.
11. Demigny, D. and Kamlé, T. 1997. A discrete expression of Canny's criteria for step edge detector performances evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(11):1199-1211.
12. Deriche, R. 1987. Using Canny's criteria to derive a recursively implemented optimal edge detector. International Journal of Computer Vision, 1(2): 167-187.
13. Deriche, R. 1993. Recursively implementing the Gaussian and its derivatives. Technical Report, INRIA.
14. Deriche, R. and Blaszka, T. 1993. Recovering and characterizing image features using an efficient model based approach. In Proceedings of the Conference on Computer Vision and Pattern Recognition, New York, USA, pp. 530-535.
15. Deriche, R. and Giraudon, G. 1993. A computational approach for corner and vertex detection. International Journal of Computer Vision, 10(2):101-124.
16. Dreschler, L. and Nagel, H.H. 1982. Volumetric model and 3D trajectory of a moving car derived from monocular TV frame sequences of a street scene. Computer Graphics and Image Processing, 20:199-228.
17. Förstner, W. 1994. A framework for low level feature extraction. In Proceedings of the 3rd European Conference on Computer Vision, Stockholm, Sweden, pp. 383-394.
18. Förstner, W. and Gülch, E. 1987. A fast operator for detection and precise location of distinct points, corners and centres of circular features. In Intercommission Conference on Fast Processing of Photogrammetric Data, Interlaken, Switzerland, pp. 281-305.
19. Harris, C. and Stephens, M. 1988. A combined corner and edge detector. In Alvey Vision Conference, pp. 147-151.
20. Heath, M.D., Sarkar, S., Sanocki, T., and Bowyer, K.W. 1997. A robust visual method for assessing the relative performance of edge-detection algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(12):1338-1359.
21. Heitger, F., Rosenthaler, L., von der Heydt, R., Peterhans, E., and Kuebler, O. 1992. Simulation of neural contour mechanism: From simple to end-stopped cells. Vision Research, 32(5):963-981.
22. Heyden, A. and Rohr, K. 1996. Evaluation of corner extraction schemes using invariance methods. In Proceedings of the 13th International Conference on Pattern Recognition, Vienna, Austria, Vol. I, pp. 895-899.
23. Horaud, R., Skordas, T., and Veillon, F. 1990. Finding geometric and relational structures in an image. In Proceedings of the 1st European Conference on Computer Vision, Antibes, France, pp. 374-384.
24. Kitchen, L. and Rosenfeld, A. 1982. Gray-level corner detection. Pattern Recognition Letters, 1:95-102.
25. Koenderink, J.J. and van Doom, A.J. 1987. Representation of local geometry in the visual system. Biological Cybernetics, 55:367-375.
26. Laganière, R. 1998. Morphological corner detection. In Proceedings of the 6th International Conference on Computer Vision, Bombay, India, pp. 280-285.
27. Lopez, A.M., Lumbreras, F., Serrat, J., and Villanueva, J.J. 1999. Evaluation of methods for ridge and valley detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21 (4):327-335.
28. Medioni, G. and Yasumoto, Y. 1987. Corner detection and curve representation using cubic B-splines. Computer Vision, Graphics and Image Processing, 39:267-278.
29. Mokhtarian, F. and Mackworth, A. 1986. Scale-based description and recognition of planar curves and two-dimensional shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(1):34-43.
30. Mokhtarian, F. and Suomela, R. 1998. Robust image corner detection through curvature scale space. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(12):1376-1381.
31. Moravec, H.P. 1977. Towards automatic visual obstacle avoidance. In Proceedings of the 5th International Joint Conference on Artificial Intelligence, Cambridge, Massachusetts, USA, p. 584.
32. Nagel, H.H. 1983. Displacement vectors derived from second order intensity variations in image sequences. Computer Vision, Graphics and Image Processing, 21:85-117.
33. Papoulis, A. 1991. Probability, Random Variables, and Stochastic Processes. McGraw Hill.
34. Parida, L., Geiger, D., and Hummel, R. 1998. Junctions: Detection, classification, and reconstruction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(7):687-698.
35. Perona, P. 1995. Deformable kernels for early vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(5):488-499.
36. Phillips, P.J. and Bowyer, K.W. 1999. Introduction to the special section on empirical evaluation of computer vision algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(4):289-290.
37. Pikaz, A. and Dinstein, I. 1994. Using simple decomposition for smoothing and feature point detection of noisy digital curves. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(8):808-813.
38. Reisfeld, D., Wolfson, H., and Yeshurun, Y. 1995. Context-free attentional operators: The generalized symmetry transform. International Journal of Computer Vision, 14(2):119-130.
39. Rohr, K. 1992. Recognizing corners by fitting parametric models. International Journal of Computer Vision, 9(3):213-230.
40. Rohr, K. 1994. Localization properties of direct corner detectors. Journal of Mathematical Imaging and Vision, 4(2):139-150.
41. Romeny, B.M., Florack, L.M.J., Salden, A.H., and Viergever, M.A. 1994. Higher order differential structure of images. Image and Vision Computing, 12(6):317-325.
42. Semple, J.G. and Kneebone, G.T. 1952. Algebraic Projective Geometry. Oxford Science Publication.
43. Shi, J. and Tomasi, C. 1994. Good features to track. In Proceedings of the Conference on Computer Vision and Pattern Recognition, Seattle, Washington, USA,pp. 593-600.
44. Shilat, E., Werman, M., and Gdalyahu, Y. 1997. Ridge's corner detection and correspondence. In Proceedings of the Conference on Computer Vision and Pattern Recognition, Puerto Rico, USA, pp. 976-981.
45. Shin, M.C., Goldgof, D., and Bowyer, K.W. 1998. An objective comparison methodology of edge detection algorithms using a structure from motion task. In Proceedings of the Conference on Computer Vision and Pattern Recognition, Santa Barbara, California, USA, pp. 190-195.
46. Shin, M.C., Goldgof, D., and Bowyer, K.W. 1999. Comparison of edge detectors using an object recognition task. In Proceedings of the Conference on Computer Vision and Pattern Recognition, Fort Collins, Colorado, USA, pp. 360-365.
47. Smith, S.M. and Brady, J.M. 1997. SUSAN - A new approach to low level image processing. International Journal of Computer Vision, 23(1):45-78.
48. Tomasi, C. and Kanade, T. 1991. Detection and tracking of point features. Technical Report CMU-CS-91-132, Carnegie Mellon University.
49. 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.