Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Detection of local invariant features using contour

This study proposes a new method for the detection of local invariant features with contour. This method differs from traditional methods that use image intensity. Image contours can be extracted stably with changes in viewpoint, scale, illumination and other factors. The proposed algorithm first extracts the stable corner from the contour, then it fits the supporting region of the contour near the corner to an angle, and uses its bisector as the direction of the feature. Next, it searches the contour for the tangent point in the direction of the angle bisector. Finally, with the corner as the centre, and in combination with the tangent point and the feature direction, an elliptic invariant region is constructed. The feasibility of the algorithm was verified experimentally by comparing its repetition rate. Test images obtained from actual scenes include several types of transformations, such as rotation, scaling, affinity, illumination and noise. The results of the experiment show the feasibility of the proposed method for use in local invariant features detection.

References

    1. 1)
      • 1. Colletto, F., Marcon, M., Sarti, A., Tubaro, S.: ‘A robust method for the estimation of reliable wide baseline correspondences’. Proc. IEEE Int. Conf. on Image Processing, Atlanta, GA, USA, 2006, pp. 10411044.
    2. 2)
      • 20. Mokhtarian, F., Suomela, R.: ‘Robust image corner detection through curvature scale space’, IEEE Trans. Pattern Anal. Mach. Intell., 1998, 20, (12), pp. 13761381 (doi: 10.1109/34.735812).
    3. 3)
      • 6. Moravec, H.: ‘Rover visual obstacle avoidance’. Proc. Int. Conf. on Artificial Intelligence, Vancouver, Canada, 1981, pp. 785790.
    4. 4)
      • 23. http://www.robots.ox.ac.uk/∼vgg/data/data-aff.html, accessed May 2012.
    5. 5)
      • 13. Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Gool, L.V.: ‘A comparison of affine region detectors’, Int. J. Comput. Vis., 2005, 65, (1), pp. 4372 (doi: 10.1007/s11263-005-3848-x).
    6. 6)
      • 7. Harris, C., Stephens, M.: ‘A combined corner and edge detector’. Proc. Alvey Vision Conf., Manchester, UK, 1988, pp. 147151.
    7. 7)
      • 5. Schmid, C., Mohr, R.: ‘Local gray value invariants for image retrieval’, IEEE Trans. Pattern. Anal. Mach. Intell., 1997, 19, (5), pp. 530535 (doi: 10.1109/34.589215).
    8. 8)
      • 15. Morel, J.M., Yu, G.: ‘ASIFT: a new framework for fully affine invariant image comparison’, SIAM J. Imag. Sci., 2009, 2, (2), pp. 438468 (doi: 10.1137/080732730).
    9. 9)
      • 11. Matas, J., Chum, O., Urban, M., Pajdla, T.: ‘Robust wide-baseline stereo from maximally stable external regions’, Image Vis. Comput., 2004, 22, (10), pp. 761767 (doi: 10.1016/j.imavis.2004.02.006).
    10. 10)
      • 12. Mikolajczyk, K., Schmid, C.: ‘A performance evaluation of local descriptors’, IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27, (10), pp. 16151630 (doi: 10.1109/TPAMI.2005.188).
    11. 11)
      • 9. Lindeberg, T., Gardinig, J.: ‘Shape-adapted smoothing in estimation of 3-D shape cues from affine deformations of local 2-D brightness structure. Image’, Vis. Comput., 1997, 15, (6), pp. 415434 (doi: 10.1016/S0262-8856(97)01144-X).
    12. 12)
      • 19. Canny, J.: ‘A computational approach to edge detection’, IEEE Trans. Pattern Anal. Mach. Intell., 1986, 8, (6), pp. 679698. (doi: 10.1109/TPAMI.1986.4767851).
    13. 13)
      • 3. Suga, A., Fukuda, K., Takiguchi, T., Ariki, Y.: ‘Object recognition and segmentation using SIFT and graph cuts’. Proc. Int. Conf. on Pattern Recognition, Tampa, Florida, USA, 2008.
    14. 14)
      • 18. Yang, D., Wang, H.X., Zhang, X.H.: ‘LoG transformation of contour curve and detection of image covariant region’, Acta Autom. Sinica, 2010, 36, (6), pp. 817822 (doi: 10.3724/SP.J.1004.2010.00817).
    15. 15)
      • 14. Tuytelaars, T., Mikolajczyk, K.: ‘Local invariant feature detectors: a survey’, Found. Trends Comput. Graph. Vis., 2007, 3, (1), pp. 177280 (doi: 10.1561/0600000017).
    16. 16)
      • 16. Yu, G., Morel, J.M.: ‘A fully affine invariant image comparison method’. Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Taipei, 2009, pp. 15971600.
    17. 17)
      • 2. Yang, Z., Guo, B.: ‘Image mosaic based on SIFT’. Proc. Int. Conf. on Intelligent Information Hiding and Multimedia Signal Processing, Harbin, China, 2008, pp. 14221425.
    18. 18)
      • 10. Mikolajczyk, K., Schmid, C.: ‘Scale & affine invariant interest point detectors’, Int. J. Comput. Vis., 2004, 60, (1), pp. 6386 (doi: 10.1023/B:VISI.0000027790.02288.f2).
    19. 19)
      • 4. Liu, J., Chen, Z., Guo, R.: ‘A mosaic method for aerial image sequence by R/C model’. Proc. Int. Conf. on Computer Science and Software Engineering, Wuhan, China, 2008, pp. 5861.
    20. 20)
      • 17. Tuytelaars, T., van Gool, L.: ‘Matching widely separated views based on affine invariant regions’, Int. J. Comput. Vis., 2004, 59, (1), pp. 6185 (doi: 10.1023/B:VISI.0000020671.28016.e8).
    21. 21)
      • 8. Lowe, D.: ‘Distinctive image features from scale-invariant keypoints detectors’, Int. J. Comput. Vis., 2004, 60, (2), pp. 91110 (doi: 10.1023/B:VISI.0000029664.99615.94).
    22. 22)
      • 21. He, X.C., Yung, N.H.C.: ‘Curvature scale space corner detector with adaptive threshold and dynamic region of support’. Proc. Int. Conf. on Pattern Recognition, Cambridge, UK, 2004, pp. 791794.
    23. 23)
      • 22. Quaddus, A., Gabbouj, M.: ‘Wavelet-based corner detection technique using optimal scale’, Pattern Recogn. Lett., 2002, 23, (1), pp. 215220 (doi: 10.1016/S0167-8655(01)00090-3).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2012.0492
Loading

Related content

content/journals/10.1049/iet-ipr.2012.0492
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address