Status of pattern recognition with wavelet analysis

Tang Yuanyan

PDF(1225 KB)
PDF(1225 KB)
Front. Comput. Sci. ›› 2008, Vol. 2 ›› Issue (3) : 268-294. DOI: 10.1007/s11704-008-0012-0

Status of pattern recognition with wavelet analysis

  • Tang Yuanyan
Author information +
History +

Abstract

Pattern recognition has become one of the fastest growing research topics in the fields of computer science and electrical and electronic engineering in the recent years. Advanced research and development in pattern recognition have found numerous applications in such areas as artificial intelligence, information security, biometrics, military science and technology, finance and economics, weather forecast, image processing, communication, biomedical engineering, document processing, robot vision, transportation, and endless other areas, with many encouraging results. The achievement of pattern recognition is most likely to benefit from some new developments of theoretical mathematics including wavelet analysis. This paper aims at a brief survey of pattern recognition with the wavelet theory. It contains the following respects: analysis and detection of singularities with wavelets; wavelet descriptors for shapes of the objects; invariant representation of patterns; handwritten and printed character recognition; texture analysis and classification; image indexing and retrieval; classification and clustering; document analysis with wavelets; iris pattern recognition; face recognition using wavelet transform; hand gestures classification; character processing with B-spline wavelet transform; wavelet-based image fusion, and others.

Cite this article

Download citation ▾
Tang Yuanyan. Status of pattern recognition with wavelet analysis. Front. Comput. Sci., 2008, 2(3): 268‒294 https://doi.org/10.1007/s11704-008-0012-0

References

1. Auslander L, Kailath T, Mitter S, eds. Signal Processing I: Signal Processing Theory. New York: Springer-Verlag, 1990
2. Beylkin G, Coifman R, Daubechies I, et al.. Wavelets and their Applications. MA: Jones and Bartlett, 1991
3. Chui C K . An Introduction to Wavelets. Boston: Academic Press, 1992
4. Daubechies I . Wavelettransform, time-frequency localization and signal analysis. IEEE Transactions Information Theory, 1990, 36: 961–1005. doi:10.1109/18.57199
5. Grossmann A, Morlet J . Decomposition of hardy functioninto square integrable wavelets of constant shape. SIAM J. Math. Anal., 1984, 15: 723–736. doi:10.1137/0515056
6. IEEE. Special issueon wavelets and signal processing. IEEETransactions on Signal Processing, 1993, 41(12): 3213–3600
7. Mallat S . Atheory of multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and MachineIntelligence, 1989, 11: 674–693. doi:10.1109/34.192463
8. Meyer Y . Ondeletteset Fonctions Splines: Seminaire EDP. EcolePolytechnique, Paris, 1986
9. SPIE. Special issueon wavelet applications. In: Harold H.Szu, editor, Proceedings of SPIE 2242, 1994
10. Chen C H, Lee J S, Sun Y N . Wavelet transformation for gray-level corner detection. Pattern Recognition, 1995, 28(6): 853–861. doi:10.1016/0031‐3203(94)00169‐M
11. Chen G, Yang Y H, Edge detection by regularizedcubic B-spline fitting. IEEE Transactionson Systems, Man and Cybernetics, April, 1995, 25(4): 636–643. doi:10.1109/21.370194
12. Chuang G C H, Kuo C C J . Wavelet descriptor of planarcurves: theory and applications”. IEEE Transactions Image Processing, 1996, 5(1): 56–70. doi:10.1109/83.481671
13. Deng W A, Lyengar S S . A new probability relaxationscheme and its application to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996, 18(4): 432–443. doi:10.1109/34.491624
14. Law T, Iton H, Seki H . Image filtering, edge detection, and edge tracing usingfuzzy reasoning. IEEE Transactions on PatternAnalysis and Machine Intelligence, 1996, 18(5): 481–491. doi:10.1109/34.494638
15. Mallat S, Hwang W L . Singularity detection andprocessing with wavelets. IEEE Transactionson Information Theory, 1992, 38: 617–643. doi:10.1109/18.119727
16. Tang Y Y, Yang L H, Feng L . Contour detection of handwriting by modular-angle-separatedwavelets. In: Proc. of the 6-th Inter.Workshop on Frontiers of Handwriting Recognition (IWFHR-VI), Taejon, Korea, August 1998, 357–366
17. Tang Y Y, Yang L H, Liu J . Wavelet-based edge detection in Chinese document. In: Proc. the 17th Int. Conf. on Computer Processingof Oriental Languages, 1997, volume 1, 333–336
18. Thune M, Olstad B, Thune N . Edge detection in noisy data using finite mixture distributeanalysis. Pattern Recognition, 1997, 30(5): 685–699. doi:10.1016/S0031‐3203(96)00115‐X
19. Tieng Q M, Boles W W . Recognition of 2D objectcontours using the wavelet transform zero-crossing representation. IEEE Transactions on Pattern Analysis and MachineIntelligence, 1997, 19: 910–916. doi:10.1109/34.608294
20. Young R K . Wavelet Theory and its Applications. Boston: Kluwer Academic Publishers, 1993
21. Tang Y Y, Li B F, Ma H, et al.. Ring-projection-wavelet-fractal signatures:a novel approach to feature extraction”. IEEE Transactions on Circuits and Systems II, 1998, 45(8): 1130–1134. doi:10.1109/82.718824
22. Tang Y Y, Liu J M, Ma H, et al.. Wavelet orthonormal decomposition for extractingfeatures in pattern recognition. InternationalJournal of Pattern Recognition and Artificial Intelligence, 1999, 13(6): 803–831. doi:10.1142/S0218001499000458
23. Tieng Q M, Boles W W . Wavelet-based affine invariantrepresentation: a tool for recognizing planar objects in 3D space. IEEE Transactions on Pattern Analysis and MachineIntelligence, 1997, 19: 846–857. doi:10.1109/34.608288
24. Wunsch P, Laine A F . Wavelet descriptors for multiresolutionrecognition of handprinted characters. Pattern Recognition, 1995, 28(8): 1237–1249. doi:10.1016/0031‐3203(95)00001‐G
25. Haley G M, Manjunath B S . Rotation-invariant textureclassification using a complete space-frequency model”. IEEE Transactions on Image Processing, 1999, 8(2): 255–269. doi:10.1109/83.743859
26. Shen D, Ip Horace H S . Discriminative wavelet shapedescriptors for recognition of 2D pattern. Pattern Recognition, 1999, 32: 151–165. doi:10.1016/S0031‐3203(98)00137‐X
27. Yoon S H, Kim J H, Alexander W E, et al.. An optimum solution for scale-invariant objectrecognition based on the multi-resolution approximation. Pattern Recognition, 1998, 31: 889–908. doi:10.1016/S0031‐3203(97)00111‐8
28. Kunte R S, Samuel R D S . Wavelet descriptors for recognitionof basic symbols in printed Kannada text. International Journal of Wavelets, Multiresolution and InformationProcessing, 2007, 5(2): 351–367. doi:10.1142/S0219691307001793
29. Lee S W, Kim C H, Ma H, et al.. Multiresolution recognition of unconstrainedhandwritten numerals with wavelet transform and multilayer clusterneural network. Pattern Recognition, 1996, 29: 1953–1961. doi:10.1016/S0031‐3203(96)00053‐2
30. Tang Y Y, Ma H, Li B, et al.. Character recognition based on doubechies wavelet. In: Proceedings of The First Int. Conf. on MultimodelInterface (ICMI'96), Beijing: Tsinghua University Press, 1996, 215–220
31. Van de Wouwer G, Schenuders P, Van Dyck D . Statistical texture characterization from discrete waveletrepresentation. IEEE Transactions on ImageProcessing, 1999, 8: 592–598. doi:10.1109/83.753747
32. Van de Wouwer G, Scheunders P, Livens S, et al.. Wavelet correlation signatures for color texturecharacterization”. Pattern Recognition, 1999, 32: 443–451. doi:10.1016/S0031‐3203(98)00035‐1
33. Liang K H, Tjahjadi T . Adaptive scale fixing formultiscale texture segmentation. IEEE Transactionson Image Processing, 2006, 15(1): 249–256. doi:10.1109/TIP.2005.860340
34. Muneeswaran K, Ganesan L, Arumugam S, et al.. A novel approach combing Gabor wavelet transformsand moments for texture segmentation. InternationalJournal of Wavelets, Multiresolution and Information Processing, 2005, 3(4): 559–572. doi:10.1142/S0219691305001020
35. Jain P, Merchant S N . Wavelet-based multiresolutionhistogram for fast image retrieval. InternationalJournal of Wavelets, Multiresolution and Information Processing, 2004, 2(1): 59–73. doi:10.1142/S0219691304000354
36. Ksantini R, Ziou D, Dubeau F, et al.. Image retrieval based on region separation andmultiresolution analysis. InternationalJournal of Wavelets, Multiresolution and Information Processing, 2006, 4(1): 147–175. doi:10.1142/S0219691306001142
37. Kubo M, Aghbari Z, Makinouchi A . Content-based image retrieval technique using wavelet-basedshift and brightness invariant edge feature. International Journal of Wavelets, Multiresolution and InformationProcessing, 2003, 1(2): 163–178. doi:10.1142/S0219691303000141
38. Moghaddam H A, Khajoie TT, Rouhi A H, et al.. Wavelet correlogram: a new approach for imageindexing and retrieval. Pattern Recognition., 2005, 38(12): 2506–2518. doi:10.1016/j.patcog.2005.05.010
39. Special Issue on Digital Library. IEEE Transactions on Pattern Analysis And Machine Intelligence, 18, 1996
40. Smeulders A W M, Worring M, Santini S, et al.. Content-based image retrieval at the end ofearly years”. IEEE Transactions onPattern Analysis and Machine Intelligence, 2000, 22: 1349–13805. doi:10.1109/34.895972
41. Murtagh F Starck J L . Pattern clustering basedon noise modeling in wavelet space. PatternRecognition, 1998, 31: 847–855. doi:10.1016/S0031‐3203(97)00115‐5
42. Shankar B U, Meher S K, AChosh. Neuro-wavelet classifier for multispectral remote sensingimages. International Journal of Wavelets,Multiresolution and Information Processing, 2007, 5(4): 589–611. doi:10.1142/S0219691307001914
43. Tang Y Y, Yang LH, Liu J M, et al.. Wavelet Theory and Its Applications to PatternRecognition. Singapore: World Scientific Publishing Co. Pte, Ltd., 2000
44. Liang K H, Chang F, Tan T M, et al.. Multiresolution hadamard representation andits application to document image analysis. In: Proceedingsof The Second Int. Conf. on Multimodel Interface (ICMI'99), Hong Kong, January 5–7 1999, V1–6
45. Tang Y Y, Liu J, Ma H, et al.. Two-dimensional wavelet transform in documentanalysis. In: Proceedings of The FirstInt. Conf. on Multimodel Interface (ICMI'96). Beijing: Tsinghua University Press, 1996, 274–279
46. Tang Y Y, Ma H, Liu J M, et al.. Multiresolution analysis in extraction of referencelines from documents with graylevel background. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19: 921–926. doi:10.1109/34.608296
47. Tang Y Y, Ma H, Xi D H, et al.. Extraction of reference lines from documentwith grey-level background using sub-image of wavelets.In: Proceedings of the 3rd International Conferenceon Document Analysis and Recognition, Montreal, Canada, Oct. 14–16 1995, 571–574
48. Daugman J . Demodulationby complex-valued wavelets for stochastic pattern recognition. International Journal of Wavelets, Multiresolutionand Information Processing, 2003, 1(1): 1–317. doi:10.1142/S0219691303000025
49. Kouzani A Z, Ong S H . Lighting-effects classificationin facial images using wavelet packets transform. International Journal of Wavelets, Multiresolution and InformationProcessing, 2003, 1(2): 199–215. doi:10.1142/S021969130300013X
50. Lai J H, Yuen P C, Feng G C . Spectroface: a Fourier-based approach for human facerecognition. In: Proceedings of The SecondInt. Conf. on Multimodel Interface (ICMI'99), 1999, VI115–120
51. Yang L H, Bui T D, Suen C Y . Image recognition based on nonlinear wavelet approximation. International Journal of Wavelets, Multiresolutionand Information Processing, 2003, 1(2): 151–161. doi:10.1142/S0219691303000104
52. Kumar S, Kumar D K . Visual hand gestures classificationusing wavelet transforms and moment based features. International Journal of Wavelets, Multiresolution and InformationProcessing, 2005, 3(1): 79–101. doi:10.1142/S0219691305000762
53. Kumar S, Kumar D K, Sharma A, et al.. Visual hand gestures classification using wavelettransforms. International Journal of Wavelets,Multiresolution and Information Processing, 2003, 1(4): 373–392. doi:10.1142/S0219691303000232
54. Sharnia A, Kumart D K, Kumar S . Wavelet directional histograms of the spatio-temporaltemplates of human gestures. InternationalJournal of Wavelets, Multiresolution and Information Processing, 2004, 2(3): 283–298. doi:10.1142/S0219691304000512
55. Yang F, Wang Z, Yu Y L . Chinese typeface generation and composition using B-splinewavelet transform. In: Proceedings ofSPIE, Wavelet Applications V Orlando, Florida, 1998, 616–620
56. El-Khamy S E, Hadhoud M M, Dessouky M I, et al.. Wavelet fusion: a tool to break the limits onLMMSE image super-resolution. InternationalJournal of Wavelets, Multiresolution and Information Processing, 2006, 4(1): 105–118. doi:10.1142/S0219691306001129
57. Li H . Wavelet-basedweighted average and human vision system image fusion. International Journal of Wavelets, Multiresolution and InformationProcessing, 2006, 4(1): 97–103. doi:10.1142/S0219691306001117
58. Li S . Multisensorremote sensing image fusion using stationary wavelet transform: effectsof basis and decomposition level. InternationalJournal of Wavelets, Multiresolution and Information Processing, 2008, 6(1): 37–50. doi:10.1142/S0219691308002203
59. Chambolle A, DeVore R A, Lee N Y, et al.. Nonlinear wavelet image processing: variationalproblems, compression, and noise removal through wavelet shrinkage. IEEE Transactions on Image Processing, 1998, 7(3): 319–335. doi:10.1109/83.661182
60. Combettes P L, Pesquet J C . Wavelet-constrained imagerestoration. International Journal of Wavelets,Multiresolution and Information Processing, 2004, 2(4): 371–389. doi:10.1142/S0219691304000688
61. Combettes P L . Convex multiresolution analysis. IEEETransactions on Pattern Analysis And Machine Intelligence, 1998, 20(12): 1308–1318. doi:10.1109/34.735804
62. Liao Z, Tang Y Y . Signal denoising using waveletsand block hidden markov model. InternationalJournal of Pattern Recognition and Artificial Intelligence, 2005, 19(5): 681–700. doi:10.1142/S0218001405004265
63. You X, Chen Q, Fang B, et al.. Thinning character using modulus minima of wavelettransform. International Journal of PatternRecognition and Artificial Intelligence, 2006, 20(3): 361–376. doi:10.1142/S0218001406004764
64. Mallat S, Zhong S . Characterization of signalsfrom multiscale edges. IEEE Transactionson Pattern Analysis and Machine Intelligence, 1992, 14(7): 710–732. doi:10.1109/34.142909
65. Tang Y Y, Yang L H, Feng L . Characterization and detection of edges by Lipschitzexponent and MASW wavelet transform. In: Proc. the 14th Int. Conf. on Pattern Recognition, Brisbane, Australia, August 1998, 1572–1574
66. Hsieh J W, Liao H M, Ko M T, et al.. Wavelet-based shape form shading. Graphical Models and Image Processing, 1995, 57(4): 343–362. doi:10.1006/gmip.1995.1030
67. Tang Y Y, Cheng H D, Suen C Y . Transformation-ring-projection (TRP) algorithm and itsVLSI implementation. International Journalof Pattern Recognition and Artificial Intelligence, 1991, 5(1 and 2): 25–56. doi:10.1142/S0218001491000053
68. Horn B K P, Brooks M J, eds. Shape from Shading. Cambridge, MA: MIT Press, 1989
69. Unser M, Aldroubi A, Eden M . On the asymptotic convergence of B-spline wavelets toGabor functions. IEEE Transactions on InformationTheory, 1992, 38: 864–872. doi:10.1109/18.119742
70. Unser M, Aldroubi A, Eden M . A family of polynomial spline wavelets transforms. Signal Processing, 1993, 30: 141–162. doi:10.1016/0165‐1684(93)90144‐Y
71. Bow S T . Pattern Recognition and Image Preprocessing. New York: Marcel-Dekker, 1992
72. Shensa M J . The discrete wavelets transform: wedding the atrous and Mallat algorithms. IEEE Transactions Signal Processing, 1992, 40: 2464–2482. doi:10.1109/78.157290
73. Starck J L, Bijaoui A, Murtagh F . Multiresolution support applied to image filtering anddeconvolution. Graphical Models Image Processing, 1995, 57: 420–431. doi:10.1006/gmip.1995.1036
74. Tang Y Y, Suen C Y, Yan C D . Document processing for automatic knowledge acquisition. IEEE Transactions on Knowledge and Data Engineering, 1994, 6(1): 3–21. doi:10.1109/69.273022
75. Mallat S G . Multifrequency channel decompositions of images and wavelet models. IEEE Transactions on Acoust. Speech Signal Process., 1989, 37(12): 2091–2110. doi:10.1109/29.45554
76. Nastar C, Ayache N . Frequency-based non-rigidmotion analysis. IEEE Transactions on PatternAnal. and Mach. Intell., 18(11), 1996
77. O'Toole A, Abdi H, Deffenbacher K, et al.. Low-dimensional representation of faces in higherdimensions of the face space. Journal ofThe Optical Society of America A., 1993, 10(3): 405–411
78. Sirovich L, Kirby M . Low-dimensional procedurefor the characterization of human faces. Journal of The Optical Society of America A., 1987, 4(3): 519–524
79. Swets D L, Weng J . Using discriminant eigenfeaturesfor image retrieval. IEEE Transactionson Pattern Analysis And Machine Intelligence, 1996, 18(8): 831–836. doi:10.1109/34.531802
80. Turk M, Pentland A . Eigenfaces for recognition. Journal of Cognitive Neuroscience, 1991, 3(1): 71–86. doi:10.1162/jocn.1991.3.1.71
81. Yuen P C, Dai D Q, Feng G C . Wavelet-based PCA for human face recognition. Proceeding of IEEE Southwest Symposium on ImageAnalysis and Interpretation, 1998, 223–228
AI Summary AI Mindmap
PDF(1225 KB)

Accesses

Citations

Detail

Sections
Recommended

/