Wavelet transforms and neural networks for compression and recognition

Neural Networks

Volumn 9, Issue 4, 1996, Pages 695-708

  Szu, Harold ^a   Telfer, Brian ^b   Garcia, Joseph ^c  

^a UNIVERSITY OF LOUISIANA AT LAFAYETTE   (United States)

^b MIT LINCOLN LABORATORY   (United States)

^c NONE

Author keywords

Adaptive wavelet transform; Data compression; Discrete wavelet transform; Neural networks; Pattern recognition

Indexed keywords

AUDITION; DATA COMPRESSION; IMAGE PROCESSING; INFRARED IMAGING; LEARNING ALGORITHMS; NEURAL NETWORKS; SENSORS; SPEECH PROCESSING; VISION; WAVELET TRANSFORMS;

CONTINUOUS WAVELET TRANSFORMS; DISCRETE WAVELET TRANSFORMS; FEATURE PRESERVING COMPRESSION; GABOR FUNCTIONS; LEARNING CONJUGATE GRADIENT DESCENT ALGORITHM; SIGNAL ENHANCED COMPRESSION;

PATTERN RECOGNITION;

ARTICLE; ARTIFICIAL NEURAL NETWORK; PATTERN RECOGNITION; PRIORITY JOURNAL;

EID: 0030176382     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/0893-6080(95)00051-8     Document Type: Article

References (39)

1. Antonini, M., Barlaud, M., Mathieu, P. & Daubechies, I. (1992). Image coding using wavelet transform. IEEE Transactions on Image Processing, 1(2), 205-220.
2. Bachmann, C. M., Musman, S. A., Luong, D. & Schultz, A. (1994). Unsupervised BCM projection pursuit algorithms for classification of simulated radar presentations. Neural Networks, 7, 709-728.
3. Baum, E. B. & Haussler, D. (1989). What size net gives valid generalization? Neural Computation, 1, 151-160.
4. Casasent, D. & Smokelin, J.-S. (1994). Neural net design of macro Gabor wavelet filters for distortion-invariant object detection in clutter. Optical Engineering, 33, 2264-2271.
5. Combes, J. M., Grossmann, A. & Tchamitchian, P. (Eds.) (1989) Wavelets: Time-frequency methods and phase space. New York: Springer.
6. Daubechies, I. (1992) Ten lectures on wavelets. Philadelphia, PA: SIAM.
7. Daniell, C. E., Kemsley, D. H., Lincoln, W. P., Tackett, W. A. & Baraghimian, G. A. (1992). Artificial neural networks for automatic target recognition. Optical Engineering, 31, 2521-2531.
8. Haar, A. (1910). Zur Theorie der orthogonalen Funktionen-systeme. Mathematical Annals, 69, 331-371.
9. Hopper, T. (1994). Compression of grey-scale fingerprint images. In Wavelet applications, Proceedings of SPIE, Vol. 2242, pp. 180-187, 5-8 April, Orlando.
10. Fogler, R. J., Koch, M. W., Moya, M. M., Hostetler, L. D. & Hush, D. R. (1992). Feature discovery via neural networks for object recognition in SAR imagery. Proceedings of the International Joint Conference on Neural Networks, Vol. IV, pp. 408-413.
11. Gorman, R. P. & Sejnowski, T. J. (1988). Analysis of hidden units in a layered network trained to classify sonar targets. Neural Networks, 1, 75-89.
12. Hampshire, J. (1993). A differential theory of learning for efficient statistical pattern recognition. Ph.D. dissertation, Carnegie Mellon University, Electrical Engineering Department.
13. Hampshire, J. & Waibel, A. (1990). A novel objective function for improved phoneme recognition using time delay neural networks. IEEE Transactions on Neural Networks, 1, 216-228.
14. MIT Lincoln Laboratory Journal (1993). Special issue on automatic target recognition, Spring, Vol. 6.
15. Rioul, O. & Duhamel, P. (1992). Fast algorithms for discrete and continuous wavelet transforms. IEEE Trans. Info. Theory, 38, 569-586.
16. Roth, M. W. (1990). Survey of neural network technology for automatic target recognition. IEEE Transactions on Neural Networks, 1, 28-43.
17. Ruck, D. W., Rogers, S. K., Kabrisky, M. & Miller, J. P. (1988). Multisensor target detection and classification. Proceedings of SPIE, 931, 14-21.
18. Ruck, D. W., Rogers, S. K., Kabrisky, M., Oxley, M. E. & Suter, B. W. (1990). The multilayer perceptron as an approximation to a Bayes optimal discriminant function. IEEE Transactions on Neural Networks, 1, 296-298.
19. Lippmann, R. P., Kukolich, L. & Singer, E. (1993). LNKnet: Neural network, machine-learning, and statistical software for pattern classification. The Lincoln Lab Journal, 6, 249-268.
20. Mallat, S. (1989). A theory of multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-11, 674-693.
21. Mallat, S. & Zhang, Z. (1993). Matching pursuit with time-frequency dictionaries. IEEE Transactions on Signal Processing.
22. Pati, Y. C. & Krishnaprasad, P. S. (1993). Analysis and synthesis of feedforward neural networks using discrete affine wavelet transforms. IEEE Transactions on Neural Networks, 4, 73-85.
23. Strang, G. (1994) Wavelets. American Scientist, 82, 150-255.
24. Sheng, Y., Roberge, D., Szu, H. & Lu, T. (1993). Optical wavelet matched filters for shift-invariant pattern recognition. Optics Letters, 18(2), 209-301.
25. Szu, H., Telfer, B. & Kadambe, S. (1992). Neural network adaptive wavelets for signal representation and classification. Optical Engineering, 31(9), 1907-1916.
26. Szu, H. & Telfer, B. (1994). Mathematics of adaptive wavelet transforms: Relating continuous with discrete transforms. Optical Engineering, 33(7), 2111-2124.
27. Szu, H., Hsu, C., Thaker, P. & Zaghloul, M. (1994). Image wavelet transforms implemented by discrete wavelet chips. Optical Engineering, 33(7), 2310-2325.
28. Szu, H., Sheng, Y. & Chen, J. (1992). Wavelet transform as a bank of matched filters. Applied Optics, 31(6), 3267-3277.
29. Szu, H., Yang, X.-Y., Telfer, B. & Sheng, Y. (1993). Neural network and wavelet transform for scale-invariant data classification. Physical Reviews E, 48(2), 1497-1501.
30. Szu, H. & Hartley, R. (1987a). Fast simulated annealing. Physical Letters A, 122, 157-162.
31. Szu, H. & Hartley, R. (1987b). Nonconvex optimization by fast simulated annealing. Proceedings of the IEEE, 75, 1538-1540.
32. Szu, H. & Messner, R. (1986). Adaptive invariant novelty filters. Proceedings of the IEEE, 74, 519-520.
33. Sadjadi, F. (1991), Automatic object recognition: Critical issues and current approaches. Proceedings of SPIE, 1471, 303-313.
34. Telfer, B. & Szu, H. (1994). Energy functions for minimizing misclassification error with minimum-complexity networks. Neural Networks, 7, 809-818.
35. Telfer, B., Szu, H., Dobeck, G., Garcia, J., Ko, H., Dubey, A. & Witherspoon, N. (1994). Adaptive wavelet classification of acoustic backscatter and imagery. Optical Engineering, 33, 2192-2203.
36. Thoet, W. A., Rainey, T. G., Brettle, D. W., Slutz, L. A. & Weingard, F. S. (1992). ANVIL neural network program for three-dimensional automatic target recognition. Optical Engineering, 31, 2532-2539.
37. Zhang, Q. & Benveniste, A. (1992). Wavelet networks. IEEE Transactions on Neural Networks, 3, 889-898.
38. Vetterli, M. & Herley, C. (1992) Wavelets and filter bank: theory and design. IEEE Transactions on Signal Processing, 40(9), 2207-2232.
39. Maes, S. & Daubechies, I. (1995). Private communication for SPIE Wavelet Applications Conference (Chair Szu) April 17-20, Orlando.