access icon free Whitening central projection descriptor for affine-invariant shape description

A novel descriptor, referred to as the whitening central projection predictor (WCPD), is developed for affine-invariant shape description. The proposed descriptor is based on central projection transform (CPT) and whitening transform (WT). Dislike contour-based or region-based approaches, an object is first converted to a closed curve by CPT, which is called the general curve (GC). The derived GC not only keeps the affine transform information, but also is very robust to noise. Then WT is performed to the GC with the purpose that the affine transformation is simplified to a rotation only. Finally, Fourier descriptors are employed to remove the rotation, and WCPD is obtained. One advantage of using WCPD for affine-invariant description lies in that it is applicable to objects consisting of several components. Furthermore, the approach used on the GC is contour-based, and is of small computational complexity. Several experiments have been conducted to evaluate the performance of the proposed method. Experimental results show that the proposed method has a powerful discrimination ability, and is more robust to noise.

Inspec keywords: shape recognition; image classification; computational complexity; edge detection; affine transforms; object detection; Fourier transforms

Other keywords: closed curve; computational complexity; affine transform; WT; Fourier descriptors; whitening central projection descriptor; whitening transform; affine-invariant description; general curve; GC; affine-invariant shape description; central projection transform; WCPD; CPT

Subjects: Integral transforms; Image recognition; Computer vision and image processing techniques; Computational complexity; Integral transforms

References

    1. 1)
      • 3. Liu, G., Lin, Z., Yu, Y.: ‘Radon representation-based feature descriptor for texture classification’, IEEE Trans. Image Process., 2009, 18, pp. 921928.
    2. 2)
      • 19. Rahtu, E., Salo, M., Heikkila, J.: ‘Affine invariant pattern recognition using multiscale autoconvolution’, IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27, pp. 908918.
    3. 3)
      • 31. Tao, Y., Lam, E.C.M., Tang, Y.Y.: ‘Feature extraction using wavelet and fractal’, Pattern Recognit. Lett., 2001, 22, pp. 271287.
    4. 4)
      • 34. Yang, M.Q., Kpalma, K., Ronsin, J.: ‘Affine invariance contour descriptor based on the equal area normalization’, LAENG Int. J. Appl. Math., 2007, 36, (2).
    5. 5)
      • 8. Vizireanu, D.N., Udrea, R.M.: ‘Visual-oriented morphological foreground content grayscale frames interpolation method’, J. Electron. Imaging, 2009, 18, pp. 13.
    6. 6)
      • 1. Gonzalez, R.C., Woods, R.E.: ‘Digital image processing’ (Addsion, 2001).
    7. 7)
      • 11. Zhang, D.S., Lu, G.J.: ‘Review of shape representation and description techniques’, Pattern Recognit., 2004, 37, pp. 119.
    8. 8)
      • 13. Arbter, K., Snyder, W.E., Burkhardt, H., Hirzinger, G.: ‘Application of affine-invariant Fourier descriptors to recognition of 3D objects’, IEEE Trans. Pattern Anal. Mach. Intell., 1990, 12, (7), pp. 640647.
    9. 9)
      • 33. Jafari-Khouzani, K., Soltanian-Zadeh, H.: ‘Rotation-invariant multiresolution texture analysis using radon and wavelet transforms’, IEEE Trans. Image Process., 2005, 14, pp. 783795.
    10. 10)
      • 28. Stolpner, S., Whitesides, S., Siddiqi, K.: ‘Sampled medial loci for 3D shape representation’, Comput. Vis. Image Underst., 2011, 115, pp. 695706.
    11. 11)
      • 17. Rube, I.E., Ahmed, M., Kamel, M.: ‘Wavelet approximation-based affine invariant shape representation functions’, IEEE Trans. Pattern Anal. Mach. Intell., 2006, 28, (2), pp. 323327.
    12. 12)
      • 24. Hu, M.K.: ‘Visual pattern recognition by moment invariants’, IRE Trans. Inf. Theory, 1962, 8, (2), pp. 179187.
    13. 13)
      • 9. Wu, Z.P., Xu, Q.Q., Jiang, S.Q., Huang, Q.M., Cui, P., Li, L.: ‘Adding affine invariant geometric constraint for partial-duplicate image retrieval’. Proc. Int. Conf. on Pattern Recognition, Istanbul, Turkey, 2010, pp. 842845.
    14. 14)
      • 27. Giblin, P.J., Sapiro, G.: ‘Affine invariant medial axis and skew symmetry’. Proc. Int. Conf. on Computer Vision, Bombay, India, 1998, pp. 833838.
    15. 15)
      • 32. Tang, Y.Y., Tao, Y., Lam, E.C.M.: ‘New method for extraction based on fractal behavior’, Pattern Recognit., 2002, 35, pp. 10711081.
    16. 16)
      • 10. Amanatiadis, A., Kaburlasos, V.G., Gasteratos, A., Papadakis, S.E.: ‘Evaluation of shape descriptors for shape-based image retrieval’, IET Image Process., 2011, 5, pp. 493499.
    17. 17)
      • Giblin, P.J., Sapiro, G.: `Affine invariant medial axis and skew symmetry', Proc. Int. Conf. on Computer Vision, 1998, Bombay, India, p. 833–838.
    18. 18)
    19. 19)
    20. 20)
    21. 21)
    22. 22)
      • Heikkilä, J.: `Multi-scale autoconvolution for affine invariant pattren recognition', Proc. Int. Conf. on Pattern Recognition, 2002, Québec, Canada, p. 119–122.
    23. 23)
      • M.Q. Yang , K. Kpalma , J. Ronsin . Affine invariance contour descriptor based on the equal area normalization. LAENG Int. J. Appl. Math. , 2
    24. 24)
    25. 25)
    26. 26)
    27. 27)
    28. 28)
    29. 29)
    30. 30)
      • 6. Ma, F., Chang, C.Q., Hung, Y.S.: ‘A subspace approach for matching 2D shapes under affine distortions’, Pattern Recognit., 2011, 44, pp. 210221.
    31. 31)
      • 2. Tieng, Q.M., Boles, W.W.: ‘Wavelet-based affine invariant representation: a tool for recognizing planar objects in 3D space’, IEEE Trans. Pattern Anal. Mach. Intell., 1997, 19, (8), pp. 12871296.
    32. 32)
      • 39. Nene, S.A., Nayar, S.K., Murase, H.: ‘Columbia Object Image Library (COIL-20)’. Technical Report. CUCS-005-96, 1996. The database can be downloaded from: available in http://www.cs.columbia.edu/CAVE/software/softlib/coil-20.php.
    33. 33)
      • 18. Tzimiropoulos, G., Mitianoudis, N., Stathaki, T.: ‘Robust recognition of planar shapes under affine transforms using principal component analysis’, IEEE Signal Process. Lett., 2007, 14, pp. 723726.
    34. 34)
      • 20. Flusser, J., Suk, T.: ‘Pattern recognition by affine moment invariants’, Pattern Recognit., 1993, 26, (1), pp. 167174.
    35. 35)
      • 36. Heikkilä, J.: ‘Pattern matching with affine moment descriptors’, Pattern Recognit., 2004, 37, pp. 18251834.
    36. 36)
      • 38. Krapac, J., Petkovic, T.: ‘Shape description with Fourier descriptors’. Tech. Report., 2002.
    37. 37)
      • 26. Petrou, M., Kadyrov, A.: ‘Affine invariant features from trace transform’, IEEE Trans. Pattern Anal. Mach. Intell., 2004, 26, (1), pp. 3044.
    38. 38)
      • 4. Lin, Y.T., Huang, C.Y., Lee, G.C.: ‘Rotation, scaling, and translation resilient watermarking for images’, IET Image Process., 2011, 5, pp. 328340.
    39. 39)
      • 37. Zuliani, M., Bertelli, L., Kenney, C.S., Chandrasekaran, S., Manjunath, B.S.: ‘Drums, curve descriptors and affine invariant region matching’, Image Vis. Comput., 2008, 26, pp. 347360.
    40. 40)
      • 21. Reiss, T.H.: ‘The revised fundamental theorem of moment invariants’, IEEE Trans. Pattern Anal. Mach. Intell., 1991, 13, (8), pp. 830834.
    41. 41)
      • 40. Nene, S.A., Nayar, S.K., Murase, H.: ‘Columbia Object Image Library (COIL-100)’. Tech. Report, CUCS-006-96, 1996. The database can be downloaded from: available in http://www.cs.columbia.edu/CAVE/software/softlib/coil-100.php.
    42. 42)
      • 16. Lin, W.S., Fang, C.H.: ‘Syntheszed affine invariant function for 2D shape recognition’, Pattern Recognit., 2007, 40, pp. 19211928.
    43. 43)
      • 12. Capar, A., Kurt, B., Gōkmen, M.: ‘Gradient based shape descriptors’, Mach. Vis. Appl., 2008, 29, pp. 14271432.
    44. 44)
      • 29. Heikkilä, J.: ‘Multi-scale autoconvolution for affine invariant pattren recognition’. Proc. Int. Conf. on Pattern Recognition, Québec, Canada, 2002, pp. 119122.
    45. 45)
      • 30. Lan, R.S., Yang, J.W., Jiang, Y., et al.: ‘An affine invariant discriminate analysis with canonical correlation analysis’, Neurocomputing, 2012, 86, pp. 184192.
    46. 46)
      • 5. Nasir, I., Khelifi, F., Jiang, J., Ipson, S.: ‘Robust image watermarking via geometrically invariant feature points and image normalisation’, IET Image Process., 2012, 6, pp. 354363.
    47. 47)
      • 14. Khalil, M.I., Bayoumi, M.M.: ‘Affine invariants for object recognition using the wavelet transform’, Pattern Recognit. Lett., 2002, 23, pp. 5772.
    48. 48)
      • 7. Udrea, R.M., Vizireanu, D.N.: ‘Iterative generalization of morphological skeleton’, J. Electron. Imaging, 2007, 16, pp. 13.
    49. 49)
      • 35. Cyganski, D., Vaz, R.F.: ‘A linear signal decomposition approach to affine invariant contour identification’, Pattern Recognit., 1995, 28, pp. 18451853.
    50. 50)
      • 15. Khalil, M.I., Bayoumi, M.M.: ‘A dyadic wavelet affine invariant function for 2D shape recognition’, IEEE Trans. Pattern Anal. Mach. Intell., 2001, 23, (10), pp. 11521163.
    51. 51)
      • 25. Ben-Arie, J., Wang, Z.: ‘Pictorial recognition of objects employing affine invariance in the frequency domain’, IEEE Trans. Pattern Anal. Mach. Intell., 1998, 20, (6), pp. 604618.
    52. 52)
      • 23. Suk, T., Flusser, J.: ‘Affine moment invariants generated by graph method’, Pattern Recognit., 2011, 44, pp. 20472056.
    53. 53)
      • 22. Yang, Z., Cohen, F.: ‘Cross-weighted moments and affine invariants for image registration and matching’, IEEE Trans. Pattern Anal. Mach. Intell., 1999, 21, (8), pp. 804814.
    54. 54)
    55. 55)
    56. 56)
    57. 57)
      • R.C. Gonzales , R.E. Woods . (1993) Digital image processing.
    58. 58)
    59. 59)
      • Wu, Z.P., Xu, Q.Q., Jiang, S.Q., Huang, Q.M., Cui, P., Li, L.: `Adding affine invariant geometric constraint for partial-duplicate image retrieval', Proc. Int. Conf. on Pattern Recognition, 2010, Istanbul, Turkey, p. 842–845.
    60. 60)
    61. 61)
    62. 62)
    63. 63)
      • Q.M. Tieng , W.W. Boles . Wavelet-based affine invariant representation: a tool for recognizing planar objects in 3D space. IEEE Trans. Pattern Anal. Mach. Intell. , 8 , 1287 - 1296
    64. 64)
      • A. Capar , B. Kurt , M. Gōkmen . Gradient based shape descriptors. Mach. Vis. Appl. , 1427 - 1432
    65. 65)
      • Nene, S.A., Nayar, S.K., Murase, H.: `Columbia Object Image Library (COIL-100)', Tech. Report, CUCS-006-96, 1996, The database can be downloaded from: available in http://www.cs.columbia.edu/CAVE/software/softlib/coil-100.php.
    66. 66)
    67. 67)
      • Krapac, J., Petkovic, T.: `Shape description with Fourier descriptors', Tech. Report, 2002.
    68. 68)
    69. 69)
    70. 70)
      • Nene, S.A., Nayar, S.K., Murase, H.: `Columbia Object Image Library (COIL-20)', Technical Report. CUCS-005-96, 1996, The database can be downloaded from: available in http://www.cs.columbia.edu/CAVE/software/softlib/coil-20.php.
    71. 71)
    72. 72)
    73. 73)
    74. 74)
    75. 75)
    76. 76)
    77. 77)
    78. 78)
    79. 79)
    80. 80)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2012.0094
Loading

Related content

content/journals/10.1049/iet-ipr.2012.0094
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading