REAL-TIME PATTERN RECOGNITION USING CIRCULAR CROSS-CORRELATION: A ROBOT VISION SYSTEM

Z. Hamici

References

  1. [1] Z. Hussain, Digital image processing: Practical applications of parallel processing techniques (Ellis Horwood, West Sussex, UK: Prentice-Hall, 1991).
  2. [2] C.F. Chiu & C. Yu Wu, The design of rotation-invariant pattern recognition using the silicon retina, IEEE Journal of Solid-State Circuits, 32 (4), 1997, 526–534. doi:10.1109/4.563674
  3. [3] A. Garrido & P. de la Blanca, Applying deformable templates for cell image segmentation, Pattern Recognition, 33 (5), 2000, 821–832. doi:10.1016/S0031-3203(99)00091-6
  4. [4] K. Voss, H. Suesse, & C. Braeuer, Affine point pattern matching, DAGM’01 Conf., Munich, Germany, September 2001, 155–162.
  5. [5] M.W. Koch, M.W. Roberts, & S.W. Aiken, A vision architecture for scale, translation, and rotation invariance, Proc. Int. Conf. on Neural Networks, vol. 2, San Diego, USA, June 1990, 393–396.
  6. [6] M.B. Reid, L. Spirkovska, & E. Ochoa, Rapid training of highorder neural networks for invariant pattern recognition, Proc. Int. Conf. on Neural Networks, vol. 1, January 1989, 689–692. doi:10.1109/IJCNN.1989.118653
  7. [7] A. Khotanzad & J. Lu, Classification of invariant image representations using a neural network, IEEE Trans. on Acoustics, Speech, and Signal Processing, 38(6), 1990, 1028–1238. doi:10.1109/29.56063
  8. [8] P. Hoyer & A. Hyvarinen, A multi-layer sparse coding network learns contour coding from natural images, Vision Research, 42 (7), 2002, 1593–1605. doi:10.1016/S0042-6989(02)00017-2
  9. [9] A.S. Aguado, M.E. Montiel, & M.S. Nixon, Arbitrary shape Hough transform by invariant geometric features, Int. Conf. on Systems, Man and Cybernetics, IEEE SMC’97, 3(5), 1997, 2661–2665. doi:10.1109/ICSMC.1997.635337
  10. [10] B. Povlow & S. Dunn, Texture classification using noncausal hidden Markov models, IEEE Trans. on Pattern Analysis and Machine Intelligence, 17(10), 1995, 1010–1014. doi:10.1109/34.464564
  11. [11] G.H. Granlund, Fourier processing for handwritten character recognition, IEEE Trans. on Computers, 21, 1992, 195–201.
  12. [12] C. Lin & R. Chellappa, Classification of partial 2-D shapes using Fourier descriptor, IEEE Trans. on Pattern Analysis and Machine Intelligence, 9, 1987, 686–690.
  13. [13] S.S. Wang, P.C. Chen, & W.G. Lin, Invariant pattern recognition by moment Fourier descriptor, Pattern Recognition, 27(12), 1994, 1735–1742. doi:10.1016/0031-3203(94)90090-6
  14. [14] C.T. Zahn & R.Z. Roskies, Fourier descriptors for plain closed curves, IEEE Trans. on Computers, 21, 1972, 269–281.
  15. [15] S.G. Mallat, A theory of multiresolution signal decomposition: The wavelet representation, IEEE Trans. on Pattern Analysis and Machine Intelligence, 11, 1989, 674–693. doi:10.1109/34.192463
  16. [16] S. Mallat & S. Zhong, Characterization of signal from multiscale edges, IEEE Trans. on Pattern Analysis and Machine Intelligence, 14(7), 1992, 710–732. doi:10.1109/34.142909
  17. [17] S.W. Lee, C.H. Kim, H. Ma, & Y.Y. Tang, Multiresolution recognition of unconstrained handwritten numerals with wavelet transform and multi-layer cluster neural network, Pattern Recognition, 29(12), 1996, 1953–1961. doi:10.1016/S0031-3203(96)00053-2
  18. [18] J. Li & C.C.J. Kuo, Automatic target shape recognition via deformable wavelet templates, Int. Symp. on Aerospace/Defense Sensing and Controls, Orlando, FL, April 8–12, 1996, 2–13.
  19. [19] P. Wunsch & A.F. Laine, Wavelet descriptors for multiresolution recognition of hand printed characters, Pattern Recognition, 28(8), 1995, 1237–1249. doi:10.1016/0031-3203(95)00001-G
  20. [20] T.D. Bui, G.Y. Chen, & L. Feng, An orthonormal shell Fourier descriptor for rapid matching of patterns in image database, International Journal of Pattern Recognition and Artificial Intelligence, 15(8), 2001, 1213–1229. doi:10.1142/S0218001401001465
  21. [21] D. Ziou & S. Tabblne, A multi-scale edge detector, Pattern Recognition, 26(9), 1993, 1305–1314. doi:10.1016/0031-3203(93)90137-L
  22. [22] J. Siuzdak, A single filter for edge detection, Pattern Recognition, 31(1), 1998, 1681–1686. doi:10.1016/S0031-3203(98)00029-6
  23. [23] D. Demigny & T. Kamle, Discrete expression of Canny’s criteria for step edge detector performances evaluation, IEEE Trans. on Pattern Analysis and Machine Intelligence, 19(11), 1997, 1199–1211. doi:10.1109/34.632980
  24. [24] S. Sarkar & K.L. Boyer, On optimal infinite impulse response edge detection filters, IEEE Trans. on Pattern Analysis and Machine Intelligence, 13(11), 1991, 1154–1171. doi:10.1109/34.103275
  25. [25] J. Koplowits & V. Greco, On the edge location error for local maximum and zero-crossing edge detectors, IEEE Trans. on Pattern Analysis and Machine Intelligence, 16(12), 1994, 1207–1212. doi:10.1109/34.387487
  26. [26] A.I. Russell & P.E. Beckmann, Efficient arbitrary sampling rate conversion with recursive calculation of coefficients, IEEE Trans. on Signal Processing, 50(4), 2002, 854–865. doi:10.1109/78.992131
  27. [27] Y.C. Eldar & A. Oppenheim, Filter-bank reconstruction of band-limited signals from non-uniform and generalized samples, IEEE Trans. on Signal Processing, 48(10), 2000, 2864–2875. doi:10.1109/78.869037
  28. [28] A. Aldroubi & K. Grochenig, Non-uniform sampling and reconstruction in shift-invariant paces, SIAM Review, 43(4), 2002, 585–620. doi:10.1137/S0036144501386986
  29. [29] Z. Hamici, A real-time 2-D pattern recognition using circular cross-correlation, 19th Int. Conf. on Computers and Their Applications, Seattle, 2004, 36–41.

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