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Published online by Cambridge University Press:  06 August 2009

B. V. K. Vijaya Kumar
Affiliation:
Carnegie Mellon University, Pennsylvania
Abhijit Mahalanobis
Affiliation:
Lockheed Martin Missiles & Fire Control, Orlando, Florida
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Publisher: Cambridge University Press
Print publication year: 2005

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References

Duda, R. O., Hart, P. E. and Stork, D. G., Pattern Classification, 2nd edn., New York, John Wiley, 2001.Google Scholar
Fukunaga, K., Introduction to Statistical Pattern Recognition, 2nd edn., New York, Academic Press, 1990.Google Scholar
T. D. Ross and J. C. Mossing, “The MSTAR evaluation methodology,” in Algorithms for Synthetic Aperture Radar Imagery VI (ed. E. G. Zelnio), Photo-Optical Instrumentation Engineering, 3721, 1999, 703–713.
T. Sim, S. Baker, and M. Bsat, The CMU Pose, Illumination, and Expression (PIE) Database of Human Faces, Tech. Report CMU-RI-TR-01–02, Robotics Institute, Carnegie Mellon University, January 2001.
VanderLugt, A., “Signal detection by complex spatial filtering,” IEEE Transactions Information Theory, 10, 1964, 139–145.Google Scholar
North, D. O., “An analysis of the factors which determine signal/noise discrimination in pulsed carrier communication systems,” Proceedings of the IEEE, 51, 1963, 1016–1027.CrossRefGoogle Scholar
Hester, C. and Casasent, D., “Multivariant technique for multiclass pattern recognition,” Applied Optics, 19, 1980, 1758–1761.CrossRefGoogle ScholarPubMed
Kumar, B. V. K. Vijaya, “Tutorial survey of composite filter designs for optical correlators,” Applied Optics, 31, 1992, 4773–4801.CrossRefGoogle ScholarPubMed
Kumar, B. V. K. Vijaya, Savvides, M., Xie, C., Venkataramani, K., Thornton, J., and Mahalanobis, A., “Biometric verification by correlation filters,” Applied Optics, 43, 2004, 391–402.CrossRefGoogle Scholar
Applied Optics – Information Processing Division, Optical Society of America, Washington, DC.
Optical Engineering, SPIE, Bellingham, WA.
Society of Photo-Optical Instrumentation Engineers (SPIE) Conferences on Automatic Target Recognition and Optical Pattern Recognition, Orlando, FL.
Rao, C. R., Linear Statistical Inference and its Applications, New York, John Wiley, 1973.CrossRefGoogle Scholar
Oja, E., Subspace Methods of Pattern Recognition, New York, John Wiley, 1983.Google Scholar
Murakami, H. and Kumar, B. V. K. Vijaya, “Efficient calculation of primary images from a set of images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 4, 1982, 511–515.CrossRefGoogle ScholarPubMed
Strang, G., Linear Algebra and Its Applications, 3rd edn., San Diego, Harcourt Brace Johanovich, 1976.Google Scholar
Stewart, G. W., Introduction to Matrix Computations, New York, Academic Press, 1973.Google Scholar
Golub, G. H. and Loan, C. F., Matrix Computations, 2nd edn., Baltimore, MD, Johns Hopkins University Press, 1989.Google Scholar
Kreyszig, E., Advanced Engineering Mathematics, New York, Wiley, 1999.Google Scholar
Papoulis, A., Probability Theory, Random Variables and Stochastic Processes, New York, McGraw Hill, 1965.Google Scholar
Stark, H. and Wood, J., Probability and Random Processes with Applications to Signal Processing, 3rd edn., Upper Saddle River, NJ, Prentice Hall, 2002.Google Scholar
Therrien, C. W., Discrete Random Signals and Statistical Signal Processing, Englewood Cliffs, NJ, Prentice Hall, 1992.Google Scholar
Casasent, D. and Psaltis, D., “Scale invariant optical transform,” Optical Engineering, 15, 1976, 258–261.CrossRefGoogle Scholar
Araujo, H. and Dias, J. M., “An introduction to the log-polar mapping [image sampling],” Proceedings of Second Workshop on Cybernetic Vision, 1996, 139–144.Google Scholar
Cooley, J. W. and Tukey, J. W., “An algorithm for machine computation of complex Fourier series,” Mathematics of Computation, 19, 1965, 297–301.CrossRefGoogle Scholar
Oppenheim, A. V., Schafer, R. W., and Buck, J. R., Discrete-Time Signal Processing, 2nd edn., Upper Saddle River, NJ, Prentice-Hall, 1999.Google Scholar
Mitra, S. K., Digital Signal Processing: A Computer-Based Approach, New York, McGraw-Hill, 1998.Google Scholar
Burrus, C. S. and Parks, T. W., DFT/FFT and Convolution Algorithms: Theory and Implementation, New York, John Wiley, 1985.Google Scholar
Trees, H. L., Detection, Estimation and Modulation Theory: Part I, New York, John Wiley, 1968.Google Scholar
Kay, S. M., Fundamentals of Signal Processing: Estimation Theory, Upper Saddle River, NJ, Prentice Hall, 1993.Google Scholar
Rao, C. R., “Information and accuracy attainable in the estimation of statistical parameters,” Bulletin of the Calcutta Mathematical Society, 37, 1945, 81–91.Google Scholar
Goodman, J. W., Introduction to Fourier Optics, New York, McGraw-Hill, 1968.Google Scholar
D. A. Jared and K. M. Johnson, “Ferroelectric liquid crystal spatial light modulators,” in Spatial Light Modulators and Applications III, Critical Reviews of Science and Technology, ed. Effron, U., Proceedings of SPIE, Bellingham, WA, SPIE, 1150, 1989, 46–60.Google Scholar
Effron, U.et al., “Silicon liquid crystal light valve: status and issues,” Optical Engineering, 22, 1983, 682.Google Scholar
Weaver, C. S. and Goodman, J. W., “A technique for optically convolving two functions,” Applied Optics, 5, 1966, 1248–1249.CrossRefGoogle ScholarPubMed
Rau, J. E., “Detection of differences in real distributions,” Journal of the Optical Society of America, 56, 1966, 1490–1494.CrossRefGoogle Scholar
Sprague, R. A., “A review of acousto-optic signal correlators,” Optical Engineering, 16, 1977, 467–474.CrossRefGoogle Scholar
Molley, P. A. and Kast, B. A., “Automatic target recognition and tracking using an acousto-optic image correlator,” Optical Engineering, 31, 1992, 956–962.CrossRefGoogle Scholar
Kumar, B. V. K. Vijaya, Dickey, F. M., and DeLaurentis, J., “Correlation filters minimizing peak location errors,” Journal of the Optical Society of America A., 9, 1992, 678–682.CrossRefGoogle Scholar
Réfrégier, P., “Filter design for optical pattern recognition: multi-criterial optimization approach,” Optics Letters, 15, 1990, 854.CrossRefGoogle Scholar
Juday, Richard D., “Optimal realizable filters and the minimum Euclidean distance principle,” Applied Optics 32, 1993, 5100–5111.CrossRefGoogle ScholarPubMed
Horner, J. L. and Gianino, P. D., “Phase-only matched filtering,” Applied Optics 23, 1984, 812–816.CrossRefGoogle ScholarPubMed
Oppenheim, A. V. and Lim, J. S., “The importance of phase in signals,” Proceedings of the IEEE 69, 1981, 529–541.CrossRefGoogle Scholar
Kumar, B. V. K. Vijaya and Bahri, Z., “Phase-only filters with improved signal-to-noise ratio,” Applied Optics, 28, 1989, 250–257.CrossRefGoogle ScholarPubMed
Bracewell, R., Fourier Transform and its Applications, New York, McGraw-Hill, 1978.Google Scholar
Flannery, D. L., Loomis, J. S., and Milkovich, M. E., “Transform-ratio ternary phase-amplitude formulation for improved correlation discrimination,” Applied Optics, 27, 1988, 4079–4083.CrossRefGoogle ScholarPubMed
J. A. Davis and J. M. Waas, “Current status of magneto-optic spatial light modulator,” in Spatial Light Modulators and Applications III, Critical Reviews of Science and Technology, ed., Effron, U., Proceedings of SPIE, Bellingham, WA, SPIE, 1150, 1989, 27–43.Google Scholar
Dickey, F. M. and Hansche, B. D., “Quad-phase correlation filters for pattern recognition,” Applied Optics, 28, 1989, 1611–13.CrossRefGoogle ScholarPubMed
Dickey, F. M., Kumar, B. V. K. Vijaya, Romero, L., and Connelly, J. M., “Complex ternary matched filters yielding high signal-to-noise ratios,” Optical Engineering, 29, 1990, 994–1001.Google Scholar
Kumar, B. V. K. Vijaya and Connelly, J. M., “Effects of Quantizing the Phase in Correlation Filters,” Proceedings of SPIE, Bellingham, WA, SPIE, 1151, 1989, 166–173.CrossRefGoogle Scholar
Kumar, B. V. K. Vijaya, Juday, Richard D., and Rajan, P. K., “Saturated filters,” Journal of the Optical Society of America A., 9, 1992, 405–412.CrossRefGoogle Scholar
Juday, Richard, “Generality of matched filtering and minimum Euclidean distance projection for optical pattern recognition,” Journal of the Optical Society of America A., 18, 2001, 1882–1896.CrossRefGoogle ScholarPubMed
Florence, J. M., “Design considerations for phase-only filters,” Proceedings of SPIE, Bellingham, WA, SPIE, 1151, 1989, 195–202.CrossRefGoogle Scholar
Javidi, B., Réfrégier, P., and Willett, P., “Optimum receiver design for pattern recognition with spatially disjoint target and scene noise,” Proceedings of SPIE, Bellingham, WA, SPIE, 2026, 1993, 29–33.CrossRefGoogle Scholar
Réfrégier, P., Javidi, B., and Zhang, G., “Minimum mean-square-error filter for pattern recognition with spatially disjoint signal and scene noise,” Optics Letters, 18, 1993, 1453–1455.CrossRefGoogle ScholarPubMed
Chesnaud, C., Réfrégier, P., and Boulet, V., “Statistical region snake-based segmentation adapted to different physical noise models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 21, 1999, 1145–1157.CrossRefGoogle Scholar
Furman, A. and Casasent, D., “Sources of correlation degradation,” Applied Optics, 16, 1977, 1652–1661.Google Scholar
Kumar, B. V. K. Vijaya, Mahalanobis, A., and Takessian, A., “Optimal tradeoff circular harmonic function correlation filter methods providing controlled in-plane rotation response,” IEEE Transactions on Image Processing, 9, 2000, 1025–1034.CrossRefGoogle Scholar
Wu, R. and Stark, H., “Rotation-invariant pattern recognition using a vector reference,” Applied Optics, 23, 1984, 838–843.CrossRefGoogle Scholar
Hsu, Y. and Arsenault, H. H., “Optical pattern recognition using circular harmonic expansion,” Applied Optics, 21, 1982, 4016–4022.CrossRefGoogle ScholarPubMed
Juday, R. D. and Bourgeois, B., “Convolution-controlled rotation and scale invariance in optical correlation,” Proceedings of SPIE, Bellingham, WA, SPIE, 938, 1988, 198–205.CrossRefGoogle Scholar
Mahalanobis, A., Kumar, B. V. K. Vijaya, Casasent, D., “Minimum average correlation energy filters,” Applied Optics, 26, 1987, 3633–3640.CrossRefGoogle ScholarPubMed
Kumar, B. V. K. Vijaya, “Minimum variance synthetic discriminant functions,” Journal of the Optical Society of America A., 3, 1986, 1579–84.CrossRefGoogle Scholar
Figue, J. and Réfrégier, P., “Optimality of trade-off filters,” Applied Optics, 32, 1993, 1933–1935.CrossRefGoogle ScholarPubMed
Mahalanobis, A., Kumar, B. V. K. Vijaya, Song, S., Sims, S. R. F., and Epperson, J. F., “Unconstrained correlation filters,” Applied Optics, 33, 1994, 3751–3759.CrossRefGoogle ScholarPubMed
Mahalanobis, A. and Kumar, B. V. K. Vijaya, “On the optimality of the MACH filter for detection of targets in noise,” Optical Engineering, 36, 1997, 2642–2648.Google Scholar
Schils, G. F. and Sweeney, D. W., “Rotationally invariant correlation filtering,” Journal of the Optical Society of America A., 2, 1985, 1411–1418.CrossRefGoogle Scholar
Schils, G. F. and Sweeney, D. W., “Optical processor for recognition of three-dimensional targets viewed from any direction,” Journal of the Optical Society of America A., 5, 1988, 1309–1321.CrossRefGoogle Scholar
Mahalanobis, A., Kumar, B. V. K. Vijaya, and Sims, S. R. F., “Distance classifier correlation filters for multi-class automatic target recognition,” Applied Optics, 35, 1996, 3127–3133.CrossRefGoogle Scholar
Mahalanobis, A. and Kumar, B. V. K. Vijaya, “Polynomial filters for higher order correlation and multi-input information fusion,” Euro American Workshop on Optoelectronic Information Processing, Stiges, SPIE, 1997, 221–231.Google Scholar
Ravichandran, G. and Casasent, D., “Advanced in-plane rotation-invariant correlation filters,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 16, 1994, 415–420.CrossRefGoogle Scholar
Garcia, J., Campos, J., and Ferreira, C., “Circular-harmonic minimum average correlation energy filter for color pattern recognition,” Applied Optics, 33, 1994, 2180–2187.CrossRefGoogle ScholarPubMed
Fisher, J. and Principe, J., “Recent advances to nonlinear MACE filters,” Optical Engineering, 36, 1997, 2697–2709.CrossRefGoogle Scholar
Gualdrón, O., Nicolás, J., Campos, J., and Yzuel, M. J., “Rotation invariant color pattern recognition by use of a three-dimensional Fourier transform,” Applied Optics, 42, 2003, 1434–1440.CrossRefGoogle ScholarPubMed
Hassebrook, L., Kumar, B. V. K. Vijaya, and Hostetler, L., “Linear phase coefficient composite filter banks for distortion-invariant optical pattern recognition,” Optical Engineering, 29, 1990, 1033–1043.Google Scholar
Ravichandran, G., and Casasent, D., “Minimum noise and correlation energy optical correlation filter,” Applied Optics, 31, 1992, 1823–1833.CrossRefGoogle ScholarPubMed
Goodman, J. W., Statistical Optics, New York, John Wiley, 1985.Google Scholar
O'Neill, E. L., Introduction to Statistical Optics, New York, Dover, 1992.Google Scholar
Azzam, R. M. A. and Bashara, N. M., Ellipsometry and Polarized Light, New York, North Holland, 1987.Google Scholar
Wolf, E., Proceedings of the Royal Society of London A 230, 1955, 246.CrossRef
Mandel, L. and Wolf, E., Optical Coherence and Quantum Optics, New York, Cambridge University Press, 1995.CrossRefGoogle Scholar
R. Juday et al., “Full-face full-complex characterization of a reflective SLM,” in Optical Pattern Recognition XI, ed. Casasent, D. P. and Chao, T. H., Proceedings of SPIE, Bellingham WA, SPIE, 4043, 2000, 80–89.Google Scholar
Cover, T. and Thomas, J., Elements of Information Theory, New York, Wiley, 1991.CrossRefGoogle Scholar
Jain, A., Fundamentals of Digital Image Processing, Englewood Cliffs, NJ, Prentice-Hall, 1989.Google Scholar
Gregory, D. A., Kirsch, J. C., and Tam, E. C., “Full complex modulation using liquid crystal televisions,” Applied Optics 31, 1992, 163–165.CrossRefGoogle ScholarPubMed
J. M. Florence, Spatial Light Modulator with Full Complex Capability, US Patent Specification 5148157, September 15, 1992.
R. D. Juday, Full Complex Modulation using Two One-Parameter Spatial Light Modulators, US Patent Specification 5416618, May 16, 1995.
Florence, J. M. and Juday, R. D., “Full complex spatial filtering with a phase mostly DMD,” Proceedings of SPIE, Bellingham, WA, SPIE, 1558, 1991.CrossRefGoogle Scholar
Juday, R. D. and Florence, J. M., “Full complex modulation with two one-parameter SLMs,” Proceedings of SPIE, Bellingham, WA, SPIE, 1558, 1991.CrossRefGoogle Scholar
Cohn, R. W., “Pseudo-random encoding of complex-valued functions onto amplitude-phase coupled modulators,” Journal of the Optical Society of America A., 15, 1998 868–883.CrossRefGoogle Scholar
Zeile, C. and Lüder, E., “Complex transmission of liquid crystal spatial light modulators in optical signal processing applications,” 1911, 1993, 195–206.Google Scholar
Biometrics: Personal Identification in Networked Society, ed., Jain, A., Bolle, R. and Pankanti, S., Norwell, MA, Kluwer, 1999.CrossRefGoogle Scholar
Soutar, C., Roberge, D., Stoianov, A., Gilroy, R., and Vijaya Kumar, B. V. K., “Optimal trade-off filter for the correlation of fingerprints,” Optical Engineering, 38, 1999, 108–113.CrossRefGoogle Scholar
Mahalanobis, A. and Singh, H., “Application of correlation filters for texture recognition,” Applied Optics, 33, 1994, 2173–2179.CrossRefGoogle ScholarPubMed
Mahalanobis, A., Kumar, B. V. K. Vijaya, and Carlson, D. W., “Evaluation of MACH and DCCF correlation filters for SAR ATR using the MSTAR Public Data Base,”Algorithms for Synthetic Aperture Radar Imagery V, Proceedings of SPIE, 3370, 1998, 460–468.Google Scholar
Turk, M. and Pentland, A., “Eigenfaces for recognition,” Journal of Cognitive Neuroscience, 3, 1991, 71–86.CrossRefGoogle ScholarPubMed
Advanced Multimedia Processing Lab of Carnegie Mellon University Electrical and Computer Engineering Department, http://amp.ece.cmu.edu.
Huang, F. J. and Chen, T., “Tracking of multiple faces for human-computer interfaces and virtual environments,” IEEE International Conference on Multimedia and Expo., New York, IEEE, 2000, 1563–1566.CrossRefGoogle Scholar

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