Abstract

We introduce subband correlation filters (SCFs) as a solution to the problem of object recognition at multiple resolution levels in quantized transformed imagery. The approach synthesizes correlation filters that operate directly on subband coefficients rather than on image data. We explore two techniques to accomplish the reduced-resolution recognition: (1) training the correlation filters to incorporate downsampling tolerance and (2) adaptation of the subband decomposition filters to accommodate the reduced resolutions. For compression ratios of 20:1, SCFs demonstrate recognition performance of at least 90%, 85%, and 75%, respectively, on 2-, 4-, and 8-ft-resolution synthetic aperture radar data.

© 2003 Optical Society of America

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  1. A. Mahalanobis, B. V. K. Vijaya Kumar, “Polynomial filters for higher order correlation and multi-input information fusion,” in Optoelectronic Information Processing (SPIE, Bellingham, Wash., 1997), pp. 221–231.
  2. B. Walls, A. Mahalanobis, “Performance of the MACH filter and DCCF algorithms in the presence of data compression,” in Automatic Target Recognition IX, F. A. Sadjadi, ed., Proc. SPIE3718, 376–387 (1999).
    [CrossRef]
  3. T-C. Liu, S. Mitra, “Fingerprint recognition of wavelet-based compressed images by neuro-fuzzy clustering,” in Applications of Fuzzy Logic Technology III, B. Bosacchi, J. C. Bezdek, eds., Proc. SPIE2761, 76–86 (1996).
    [CrossRef]
  4. F. B. Shin, D. H. Kil, “Integrated approach to bandwidth reduction and mine detection in shallow water with reduced-dimension image compression and automatic target recognition algorithms,” in Detection and Remediation Technologies for Mines and Minelike Targets II, A. C. Dubey, R. L. Barnard, eds., Proc. SPIE3079, 203–212 (1997).
    [CrossRef]
  5. P. C. Miller, “Reduced-resolution synthetic-discriminant-function design by multiresolution wavelet analysis,” Appl. Opt. 34, 865–878 (1995).
    [CrossRef] [PubMed]
  6. D. O. North, “An analysis of the factors which determine signal/noise discrimination in pulsed carrier systems,” Proc. IEEE 51, 1016–1027 (1963).
    [CrossRef]
  7. G. L. Turin, “An introduction to matched filters,” IRE Trans. Inf. Theor. 6, 311–329 (1960).
    [CrossRef]
  8. H. Andrews, Computer Techniques in Image Processing, (Academic, New York, 1970), pp. 55–71.
  9. R. O. Duda, P. E. Hart, Pattern Classification and Scene Analysis, (Wiley-Interscience, New York, 1973), pp. 276–284.
  10. W. K. Pratt, Digital Image Processing, (Wiley, New York, 1978), Parts 5 and 6.
  11. C. F. Hester, D. Casasent, “Multivariant technique for multiclass pattern recognition,” Appl. Opt. 19, 1758–1761 (1980).
    [CrossRef] [PubMed]
  12. B. V. K. Vijaya Kumar, A. Mahalanobis, R. Juday, Correlation Pattern Recognition (Cambridge U. Press, Cambridge) (to be published).
  13. B. V. K. Vijaya Kumar, “Tutorial survey of composite filter designs for optical correlators,” Appl. Opt. 31, 4773–4801 (1992).
    [CrossRef]
  14. B. V. K. Vijaya Kumar, “Minimum-variance synthetic discriminant functions,” J. Opt. Soc. Am. A 3, 1579–1584 (1986).
    [CrossRef]
  15. A. Mahalanobis, “New correlation filters for symbolic and rule based pattern recognition,” Ph.D. thesis (Carnegie Mellon University, Pittsburgh, Pa., 1987).
  16. A. Mahalanobis, B. V. K. Vijaya Kumar, D. Casasent, “Minimum average correlation energy filters,” Appl. Opt. 26, 3633–3640 (1987).
    [CrossRef] [PubMed]
  17. A. Mahalanobis, B. Vijaya Kumar, “Optimality of the maximum average correlation height filter for detection of targets in noise,” Opt. Eng. 36, 2642–2648 (1997).
    [CrossRef]
  18. K. G. Leib, J. C. Mendelsohn, “Application of the matched filter image correlator to IR, SAR, and visual target data,” in Proc. SPIE 519, 96–105 (1985).
    [CrossRef]
  19. W. M. Boerner, A. B. Kostinski, “On the concept of the polarimetric matched filter in high resolution radar imaging,” in Proceedings of the IEEE 1988 International Symposium Digest: Antennas and Propagation (Institute of Electrical and Electronics Engineers, New York, 1988), vol. 2, pp. 533–536.
    [CrossRef]
  20. L. M. Novak, G. J. Owirka, W. S. Brower, A. L. Weaver, “The automatic target recognition system in SAIP,” Lincoln Lab. J. 10, 187–202 (1997).
  21. L. M. Novak, G. J. Owirka, W. S. Brower, “Performance of a 20-target MSE classifier,” in Radar Sensor Technology III, R. Trebits, J. L. Kurtz, eds., Proc. SPIE3395, 138–151 (1998).
    [CrossRef]
  22. L. M. Novak, G. J. Owirka, A. Weaver, “Automatic target recognition using enhanced resolution SAR data,” IEEE Trans. Aerosp. Electron. Syst. 35, 157–175 (1999).
    [CrossRef]
  23. L. Hostetler, “Template based ATR,” presented at the Fifty-Second Automatic Target Recognizer Working Group, Space and Naval Warfare Systems Center San Diego, San Diego, Calif., 8 June 1999.
  24. A. Mahalanobis, A. Forman, N. Day, M. Bower, R. Cherry, “Multiclass SAR ATR using shift-invariant correlation filters,” Pattern Recogn. 27, 619–626 (1994).
    [CrossRef]
  25. A. Mahalanobis, L. Ortiz, B. V. K. Vijaya Kumar, “Performance of the MACH filter and DCCF algorithms on the 10-class public release MSTAR data set,” in Algorithms for Synthetic Aperture Radar Imagery VI, E. G. Zelnio, ed., Proc. SPIE3721, 285–291 (1999).
    [CrossRef]
  26. A. Mahalanobis, L. Ortiz, B. V. K. Vijaya Kumar, A. Ezekial, “Correlation ATR performance using Xpatch (synthetic) training data,” in Algorithms for Synthetic Aperture Radar Imagery VII, E. G. Zelnio, ed., Proc. SPIE4053, 340–344 (2000).
    [CrossRef]
  27. K. Sayood, Introduction to Data Compression (Morgan Kaufmann, Los Altos, Calif., 1996).
  28. H. C. Andrews, W. K. Pratt, “Fourier transform coding of images,” in Proceedings of Hawaii International Conference on Systems and Science (U. Hawaii Press, Honolulu, 1968), pp. 677–679.
  29. H. C. Andrews, W. K. Pratt, “Transform image coding,” in Proceedings of the Symposium on Computer Processing in Communications (Wiley-Interscience, Chichester, UK, 1970), pp. 63–84.
  30. G. K. Wallace, “The JPEG still picture compression standard,” Commun. ACM 34, 31–44 (1991).
    [CrossRef]
  31. P. P. Vaidyanathan, Multirate Systems and Filter Banks (Prentice-Hall, Englewood Cliffs, N.J., 1993).
  32. M. A. Vetterli, J. Kovacevic, Wavelets and Subband Coding (Prentice-Hall, Englewood Cliffs, N.J., 1995).
  33. G. Strang, T. Nguyen, Wavelets and Filter Banks (Wellesley-Cambridge, Wellesley, Mass., 1996).
  34. P. P. Vaidyanathan, “Theory and design of M-channel maximally decimated quadrature mirror filters with arbitrary M, having the perfect reconstruction property,” IEEE Trans. Acoust. Speech and Signal Process. ASSP-35, 476–492 (1987).
    [CrossRef]
  35. P. P. Vaidyanathan, “Quadrature mirror filter banks, M-band extensions and perfect reconstruction techniques,” IEEE ASSP Mag. 4, June1987, pp. 4–20.
  36. M. A. Vetterli, “A theory of multirate filter banks,” IEEE Trans. Acoust. Speech Signal Process. ASSP-35, 356–372 (1987).
    [CrossRef]
  37. P. P. Vaidyanathan, “Multirate digital filters, filter banks, polyphase networks, and applications: a tutorial,” Proc. IEEE 78, 56–93 (1990).
    [CrossRef]
  38. A. Croisier, D. Esteban, C. Galand, “Perfect channel splitting by use of interpolation/decimation techniques,” in Proceedings of the First International Conference on Information Sciences and Systems (Hemisphere, Washington, D.C., 1977), pp. 443–446.
  39. D. W. Carlson, “Optimal tradeoff composite correlation filters,” Ph.D. thesis (Carnegie Mellon University, Pittsburgh, Pa., 1996).
  40. C. Daniell, “Object recognition in compressed imagery,” Ph.D. thesis (California Institute of Technology, Pasadena, California, 2000).
  41. C. E. Daniell, A. Mahalanobis, R. Goodman, “Joint optimization of correlation and subband transform filters for the dual requirements of recognition and compression,” submitted to IEEE Trans. Image Process.
  42. V. K. Jain, R. E. Crochiere, “Quadrature mirror filter design in the time domain,” IEEE Trans. Acoust. Speech Signal Process. ASSP-32, 353–360 (1984).
    [CrossRef]
  43. S. S. Hemami, R. M. Gray, “Subband filters optimized for lost coefficient reconstruction,” IEEE Trans. Signal Proc. 45, 763–767 (1997).
    [CrossRef]
  44. S. S. Hemami, “Reconstruction of compressed images and video for lossy packet networks,” Ph.D. thesis (Stanford University, Stanford, Calif., 1995).
  45. A. Mahalanobis, B. V. K. Vijaya Kumar, S. R. F. Sims, “Distance-classifier correlation filters for multiclass target recognition,” App. Opt. 35, 3127–3133 (1996).
    [CrossRef]
  46. J. M. Shapiro, “Embedded image coding using zerotrees of wavelet coefficients,” IEEE Trans. Signal Process. 41, 3445–3462 (1993).
    [CrossRef]

1999 (1)

L. M. Novak, G. J. Owirka, A. Weaver, “Automatic target recognition using enhanced resolution SAR data,” IEEE Trans. Aerosp. Electron. Syst. 35, 157–175 (1999).
[CrossRef]

1997 (3)

A. Mahalanobis, B. Vijaya Kumar, “Optimality of the maximum average correlation height filter for detection of targets in noise,” Opt. Eng. 36, 2642–2648 (1997).
[CrossRef]

S. S. Hemami, R. M. Gray, “Subband filters optimized for lost coefficient reconstruction,” IEEE Trans. Signal Proc. 45, 763–767 (1997).
[CrossRef]

L. M. Novak, G. J. Owirka, W. S. Brower, A. L. Weaver, “The automatic target recognition system in SAIP,” Lincoln Lab. J. 10, 187–202 (1997).

1996 (1)

A. Mahalanobis, B. V. K. Vijaya Kumar, S. R. F. Sims, “Distance-classifier correlation filters for multiclass target recognition,” App. Opt. 35, 3127–3133 (1996).
[CrossRef]

1995 (1)

1994 (1)

A. Mahalanobis, A. Forman, N. Day, M. Bower, R. Cherry, “Multiclass SAR ATR using shift-invariant correlation filters,” Pattern Recogn. 27, 619–626 (1994).
[CrossRef]

1993 (1)

J. M. Shapiro, “Embedded image coding using zerotrees of wavelet coefficients,” IEEE Trans. Signal Process. 41, 3445–3462 (1993).
[CrossRef]

1992 (1)

1991 (1)

G. K. Wallace, “The JPEG still picture compression standard,” Commun. ACM 34, 31–44 (1991).
[CrossRef]

1990 (1)

P. P. Vaidyanathan, “Multirate digital filters, filter banks, polyphase networks, and applications: a tutorial,” Proc. IEEE 78, 56–93 (1990).
[CrossRef]

1987 (4)

P. P. Vaidyanathan, “Theory and design of M-channel maximally decimated quadrature mirror filters with arbitrary M, having the perfect reconstruction property,” IEEE Trans. Acoust. Speech and Signal Process. ASSP-35, 476–492 (1987).
[CrossRef]

P. P. Vaidyanathan, “Quadrature mirror filter banks, M-band extensions and perfect reconstruction techniques,” IEEE ASSP Mag. 4, June1987, pp. 4–20.

M. A. Vetterli, “A theory of multirate filter banks,” IEEE Trans. Acoust. Speech Signal Process. ASSP-35, 356–372 (1987).
[CrossRef]

A. Mahalanobis, B. V. K. Vijaya Kumar, D. Casasent, “Minimum average correlation energy filters,” Appl. Opt. 26, 3633–3640 (1987).
[CrossRef] [PubMed]

1986 (1)

1985 (1)

K. G. Leib, J. C. Mendelsohn, “Application of the matched filter image correlator to IR, SAR, and visual target data,” in Proc. SPIE 519, 96–105 (1985).
[CrossRef]

1984 (1)

V. K. Jain, R. E. Crochiere, “Quadrature mirror filter design in the time domain,” IEEE Trans. Acoust. Speech Signal Process. ASSP-32, 353–360 (1984).
[CrossRef]

1980 (1)

1963 (1)

D. O. North, “An analysis of the factors which determine signal/noise discrimination in pulsed carrier systems,” Proc. IEEE 51, 1016–1027 (1963).
[CrossRef]

1960 (1)

G. L. Turin, “An introduction to matched filters,” IRE Trans. Inf. Theor. 6, 311–329 (1960).
[CrossRef]

Andrews, H.

H. Andrews, Computer Techniques in Image Processing, (Academic, New York, 1970), pp. 55–71.

Andrews, H. C.

H. C. Andrews, W. K. Pratt, “Transform image coding,” in Proceedings of the Symposium on Computer Processing in Communications (Wiley-Interscience, Chichester, UK, 1970), pp. 63–84.

H. C. Andrews, W. K. Pratt, “Fourier transform coding of images,” in Proceedings of Hawaii International Conference on Systems and Science (U. Hawaii Press, Honolulu, 1968), pp. 677–679.

Boerner, W. M.

W. M. Boerner, A. B. Kostinski, “On the concept of the polarimetric matched filter in high resolution radar imaging,” in Proceedings of the IEEE 1988 International Symposium Digest: Antennas and Propagation (Institute of Electrical and Electronics Engineers, New York, 1988), vol. 2, pp. 533–536.
[CrossRef]

Bower, M.

A. Mahalanobis, A. Forman, N. Day, M. Bower, R. Cherry, “Multiclass SAR ATR using shift-invariant correlation filters,” Pattern Recogn. 27, 619–626 (1994).
[CrossRef]

Brower, W. S.

L. M. Novak, G. J. Owirka, W. S. Brower, A. L. Weaver, “The automatic target recognition system in SAIP,” Lincoln Lab. J. 10, 187–202 (1997).

L. M. Novak, G. J. Owirka, W. S. Brower, “Performance of a 20-target MSE classifier,” in Radar Sensor Technology III, R. Trebits, J. L. Kurtz, eds., Proc. SPIE3395, 138–151 (1998).
[CrossRef]

Carlson, D. W.

D. W. Carlson, “Optimal tradeoff composite correlation filters,” Ph.D. thesis (Carnegie Mellon University, Pittsburgh, Pa., 1996).

Casasent, D.

Cherry, R.

A. Mahalanobis, A. Forman, N. Day, M. Bower, R. Cherry, “Multiclass SAR ATR using shift-invariant correlation filters,” Pattern Recogn. 27, 619–626 (1994).
[CrossRef]

Crochiere, R. E.

V. K. Jain, R. E. Crochiere, “Quadrature mirror filter design in the time domain,” IEEE Trans. Acoust. Speech Signal Process. ASSP-32, 353–360 (1984).
[CrossRef]

Croisier, A.

A. Croisier, D. Esteban, C. Galand, “Perfect channel splitting by use of interpolation/decimation techniques,” in Proceedings of the First International Conference on Information Sciences and Systems (Hemisphere, Washington, D.C., 1977), pp. 443–446.

Daniell, C.

C. Daniell, “Object recognition in compressed imagery,” Ph.D. thesis (California Institute of Technology, Pasadena, California, 2000).

Daniell, C. E.

C. E. Daniell, A. Mahalanobis, R. Goodman, “Joint optimization of correlation and subband transform filters for the dual requirements of recognition and compression,” submitted to IEEE Trans. Image Process.

Day, N.

A. Mahalanobis, A. Forman, N. Day, M. Bower, R. Cherry, “Multiclass SAR ATR using shift-invariant correlation filters,” Pattern Recogn. 27, 619–626 (1994).
[CrossRef]

Duda, R. O.

R. O. Duda, P. E. Hart, Pattern Classification and Scene Analysis, (Wiley-Interscience, New York, 1973), pp. 276–284.

Esteban, D.

A. Croisier, D. Esteban, C. Galand, “Perfect channel splitting by use of interpolation/decimation techniques,” in Proceedings of the First International Conference on Information Sciences and Systems (Hemisphere, Washington, D.C., 1977), pp. 443–446.

Ezekial, A.

A. Mahalanobis, L. Ortiz, B. V. K. Vijaya Kumar, A. Ezekial, “Correlation ATR performance using Xpatch (synthetic) training data,” in Algorithms for Synthetic Aperture Radar Imagery VII, E. G. Zelnio, ed., Proc. SPIE4053, 340–344 (2000).
[CrossRef]

Forman, A.

A. Mahalanobis, A. Forman, N. Day, M. Bower, R. Cherry, “Multiclass SAR ATR using shift-invariant correlation filters,” Pattern Recogn. 27, 619–626 (1994).
[CrossRef]

Galand, C.

A. Croisier, D. Esteban, C. Galand, “Perfect channel splitting by use of interpolation/decimation techniques,” in Proceedings of the First International Conference on Information Sciences and Systems (Hemisphere, Washington, D.C., 1977), pp. 443–446.

Goodman, R.

C. E. Daniell, A. Mahalanobis, R. Goodman, “Joint optimization of correlation and subband transform filters for the dual requirements of recognition and compression,” submitted to IEEE Trans. Image Process.

Gray, R. M.

S. S. Hemami, R. M. Gray, “Subband filters optimized for lost coefficient reconstruction,” IEEE Trans. Signal Proc. 45, 763–767 (1997).
[CrossRef]

Hart, P. E.

R. O. Duda, P. E. Hart, Pattern Classification and Scene Analysis, (Wiley-Interscience, New York, 1973), pp. 276–284.

Hemami, S. S.

S. S. Hemami, R. M. Gray, “Subband filters optimized for lost coefficient reconstruction,” IEEE Trans. Signal Proc. 45, 763–767 (1997).
[CrossRef]

S. S. Hemami, “Reconstruction of compressed images and video for lossy packet networks,” Ph.D. thesis (Stanford University, Stanford, Calif., 1995).

Hester, C. F.

Hostetler, L.

L. Hostetler, “Template based ATR,” presented at the Fifty-Second Automatic Target Recognizer Working Group, Space and Naval Warfare Systems Center San Diego, San Diego, Calif., 8 June 1999.

Jain, V. K.

V. K. Jain, R. E. Crochiere, “Quadrature mirror filter design in the time domain,” IEEE Trans. Acoust. Speech Signal Process. ASSP-32, 353–360 (1984).
[CrossRef]

Juday, R.

B. V. K. Vijaya Kumar, A. Mahalanobis, R. Juday, Correlation Pattern Recognition (Cambridge U. Press, Cambridge) (to be published).

Kil, D. H.

F. B. Shin, D. H. Kil, “Integrated approach to bandwidth reduction and mine detection in shallow water with reduced-dimension image compression and automatic target recognition algorithms,” in Detection and Remediation Technologies for Mines and Minelike Targets II, A. C. Dubey, R. L. Barnard, eds., Proc. SPIE3079, 203–212 (1997).
[CrossRef]

Kostinski, A. B.

W. M. Boerner, A. B. Kostinski, “On the concept of the polarimetric matched filter in high resolution radar imaging,” in Proceedings of the IEEE 1988 International Symposium Digest: Antennas and Propagation (Institute of Electrical and Electronics Engineers, New York, 1988), vol. 2, pp. 533–536.
[CrossRef]

Kovacevic, J.

M. A. Vetterli, J. Kovacevic, Wavelets and Subband Coding (Prentice-Hall, Englewood Cliffs, N.J., 1995).

Leib, K. G.

K. G. Leib, J. C. Mendelsohn, “Application of the matched filter image correlator to IR, SAR, and visual target data,” in Proc. SPIE 519, 96–105 (1985).
[CrossRef]

Liu, T-C.

T-C. Liu, S. Mitra, “Fingerprint recognition of wavelet-based compressed images by neuro-fuzzy clustering,” in Applications of Fuzzy Logic Technology III, B. Bosacchi, J. C. Bezdek, eds., Proc. SPIE2761, 76–86 (1996).
[CrossRef]

Mahalanobis, A.

A. Mahalanobis, B. Vijaya Kumar, “Optimality of the maximum average correlation height filter for detection of targets in noise,” Opt. Eng. 36, 2642–2648 (1997).
[CrossRef]

A. Mahalanobis, B. V. K. Vijaya Kumar, S. R. F. Sims, “Distance-classifier correlation filters for multiclass target recognition,” App. Opt. 35, 3127–3133 (1996).
[CrossRef]

A. Mahalanobis, A. Forman, N. Day, M. Bower, R. Cherry, “Multiclass SAR ATR using shift-invariant correlation filters,” Pattern Recogn. 27, 619–626 (1994).
[CrossRef]

A. Mahalanobis, B. V. K. Vijaya Kumar, D. Casasent, “Minimum average correlation energy filters,” Appl. Opt. 26, 3633–3640 (1987).
[CrossRef] [PubMed]

C. E. Daniell, A. Mahalanobis, R. Goodman, “Joint optimization of correlation and subband transform filters for the dual requirements of recognition and compression,” submitted to IEEE Trans. Image Process.

B. Walls, A. Mahalanobis, “Performance of the MACH filter and DCCF algorithms in the presence of data compression,” in Automatic Target Recognition IX, F. A. Sadjadi, ed., Proc. SPIE3718, 376–387 (1999).
[CrossRef]

A. Mahalanobis, “New correlation filters for symbolic and rule based pattern recognition,” Ph.D. thesis (Carnegie Mellon University, Pittsburgh, Pa., 1987).

A. Mahalanobis, L. Ortiz, B. V. K. Vijaya Kumar, A. Ezekial, “Correlation ATR performance using Xpatch (synthetic) training data,” in Algorithms for Synthetic Aperture Radar Imagery VII, E. G. Zelnio, ed., Proc. SPIE4053, 340–344 (2000).
[CrossRef]

A. Mahalanobis, L. Ortiz, B. V. K. Vijaya Kumar, “Performance of the MACH filter and DCCF algorithms on the 10-class public release MSTAR data set,” in Algorithms for Synthetic Aperture Radar Imagery VI, E. G. Zelnio, ed., Proc. SPIE3721, 285–291 (1999).
[CrossRef]

B. V. K. Vijaya Kumar, A. Mahalanobis, R. Juday, Correlation Pattern Recognition (Cambridge U. Press, Cambridge) (to be published).

A. Mahalanobis, B. V. K. Vijaya Kumar, “Polynomial filters for higher order correlation and multi-input information fusion,” in Optoelectronic Information Processing (SPIE, Bellingham, Wash., 1997), pp. 221–231.

Mendelsohn, J. C.

K. G. Leib, J. C. Mendelsohn, “Application of the matched filter image correlator to IR, SAR, and visual target data,” in Proc. SPIE 519, 96–105 (1985).
[CrossRef]

Miller, P. C.

Mitra, S.

T-C. Liu, S. Mitra, “Fingerprint recognition of wavelet-based compressed images by neuro-fuzzy clustering,” in Applications of Fuzzy Logic Technology III, B. Bosacchi, J. C. Bezdek, eds., Proc. SPIE2761, 76–86 (1996).
[CrossRef]

Nguyen, T.

G. Strang, T. Nguyen, Wavelets and Filter Banks (Wellesley-Cambridge, Wellesley, Mass., 1996).

North, D. O.

D. O. North, “An analysis of the factors which determine signal/noise discrimination in pulsed carrier systems,” Proc. IEEE 51, 1016–1027 (1963).
[CrossRef]

Novak, L. M.

L. M. Novak, G. J. Owirka, A. Weaver, “Automatic target recognition using enhanced resolution SAR data,” IEEE Trans. Aerosp. Electron. Syst. 35, 157–175 (1999).
[CrossRef]

L. M. Novak, G. J. Owirka, W. S. Brower, A. L. Weaver, “The automatic target recognition system in SAIP,” Lincoln Lab. J. 10, 187–202 (1997).

L. M. Novak, G. J. Owirka, W. S. Brower, “Performance of a 20-target MSE classifier,” in Radar Sensor Technology III, R. Trebits, J. L. Kurtz, eds., Proc. SPIE3395, 138–151 (1998).
[CrossRef]

Ortiz, L.

A. Mahalanobis, L. Ortiz, B. V. K. Vijaya Kumar, “Performance of the MACH filter and DCCF algorithms on the 10-class public release MSTAR data set,” in Algorithms for Synthetic Aperture Radar Imagery VI, E. G. Zelnio, ed., Proc. SPIE3721, 285–291 (1999).
[CrossRef]

A. Mahalanobis, L. Ortiz, B. V. K. Vijaya Kumar, A. Ezekial, “Correlation ATR performance using Xpatch (synthetic) training data,” in Algorithms for Synthetic Aperture Radar Imagery VII, E. G. Zelnio, ed., Proc. SPIE4053, 340–344 (2000).
[CrossRef]

Owirka, G. J.

L. M. Novak, G. J. Owirka, A. Weaver, “Automatic target recognition using enhanced resolution SAR data,” IEEE Trans. Aerosp. Electron. Syst. 35, 157–175 (1999).
[CrossRef]

L. M. Novak, G. J. Owirka, W. S. Brower, A. L. Weaver, “The automatic target recognition system in SAIP,” Lincoln Lab. J. 10, 187–202 (1997).

L. M. Novak, G. J. Owirka, W. S. Brower, “Performance of a 20-target MSE classifier,” in Radar Sensor Technology III, R. Trebits, J. L. Kurtz, eds., Proc. SPIE3395, 138–151 (1998).
[CrossRef]

Pratt, W. K.

H. C. Andrews, W. K. Pratt, “Transform image coding,” in Proceedings of the Symposium on Computer Processing in Communications (Wiley-Interscience, Chichester, UK, 1970), pp. 63–84.

W. K. Pratt, Digital Image Processing, (Wiley, New York, 1978), Parts 5 and 6.

H. C. Andrews, W. K. Pratt, “Fourier transform coding of images,” in Proceedings of Hawaii International Conference on Systems and Science (U. Hawaii Press, Honolulu, 1968), pp. 677–679.

Sayood, K.

K. Sayood, Introduction to Data Compression (Morgan Kaufmann, Los Altos, Calif., 1996).

Shapiro, J. M.

J. M. Shapiro, “Embedded image coding using zerotrees of wavelet coefficients,” IEEE Trans. Signal Process. 41, 3445–3462 (1993).
[CrossRef]

Shin, F. B.

F. B. Shin, D. H. Kil, “Integrated approach to bandwidth reduction and mine detection in shallow water with reduced-dimension image compression and automatic target recognition algorithms,” in Detection and Remediation Technologies for Mines and Minelike Targets II, A. C. Dubey, R. L. Barnard, eds., Proc. SPIE3079, 203–212 (1997).
[CrossRef]

Sims, S. R. F.

A. Mahalanobis, B. V. K. Vijaya Kumar, S. R. F. Sims, “Distance-classifier correlation filters for multiclass target recognition,” App. Opt. 35, 3127–3133 (1996).
[CrossRef]

Strang, G.

G. Strang, T. Nguyen, Wavelets and Filter Banks (Wellesley-Cambridge, Wellesley, Mass., 1996).

Turin, G. L.

G. L. Turin, “An introduction to matched filters,” IRE Trans. Inf. Theor. 6, 311–329 (1960).
[CrossRef]

Vaidyanathan, P. P.

P. P. Vaidyanathan, “Multirate digital filters, filter banks, polyphase networks, and applications: a tutorial,” Proc. IEEE 78, 56–93 (1990).
[CrossRef]

P. P. Vaidyanathan, “Theory and design of M-channel maximally decimated quadrature mirror filters with arbitrary M, having the perfect reconstruction property,” IEEE Trans. Acoust. Speech and Signal Process. ASSP-35, 476–492 (1987).
[CrossRef]

P. P. Vaidyanathan, “Quadrature mirror filter banks, M-band extensions and perfect reconstruction techniques,” IEEE ASSP Mag. 4, June1987, pp. 4–20.

P. P. Vaidyanathan, Multirate Systems and Filter Banks (Prentice-Hall, Englewood Cliffs, N.J., 1993).

Vetterli, M. A.

M. A. Vetterli, “A theory of multirate filter banks,” IEEE Trans. Acoust. Speech Signal Process. ASSP-35, 356–372 (1987).
[CrossRef]

M. A. Vetterli, J. Kovacevic, Wavelets and Subband Coding (Prentice-Hall, Englewood Cliffs, N.J., 1995).

Vijaya Kumar, B.

A. Mahalanobis, B. Vijaya Kumar, “Optimality of the maximum average correlation height filter for detection of targets in noise,” Opt. Eng. 36, 2642–2648 (1997).
[CrossRef]

Vijaya Kumar, B. V. K.

A. Mahalanobis, B. V. K. Vijaya Kumar, S. R. F. Sims, “Distance-classifier correlation filters for multiclass target recognition,” App. Opt. 35, 3127–3133 (1996).
[CrossRef]

B. V. K. Vijaya Kumar, “Tutorial survey of composite filter designs for optical correlators,” Appl. Opt. 31, 4773–4801 (1992).
[CrossRef]

A. Mahalanobis, B. V. K. Vijaya Kumar, D. Casasent, “Minimum average correlation energy filters,” Appl. Opt. 26, 3633–3640 (1987).
[CrossRef] [PubMed]

B. V. K. Vijaya Kumar, “Minimum-variance synthetic discriminant functions,” J. Opt. Soc. Am. A 3, 1579–1584 (1986).
[CrossRef]

B. V. K. Vijaya Kumar, A. Mahalanobis, R. Juday, Correlation Pattern Recognition (Cambridge U. Press, Cambridge) (to be published).

A. Mahalanobis, B. V. K. Vijaya Kumar, “Polynomial filters for higher order correlation and multi-input information fusion,” in Optoelectronic Information Processing (SPIE, Bellingham, Wash., 1997), pp. 221–231.

A. Mahalanobis, L. Ortiz, B. V. K. Vijaya Kumar, “Performance of the MACH filter and DCCF algorithms on the 10-class public release MSTAR data set,” in Algorithms for Synthetic Aperture Radar Imagery VI, E. G. Zelnio, ed., Proc. SPIE3721, 285–291 (1999).
[CrossRef]

A. Mahalanobis, L. Ortiz, B. V. K. Vijaya Kumar, A. Ezekial, “Correlation ATR performance using Xpatch (synthetic) training data,” in Algorithms for Synthetic Aperture Radar Imagery VII, E. G. Zelnio, ed., Proc. SPIE4053, 340–344 (2000).
[CrossRef]

Wallace, G. K.

G. K. Wallace, “The JPEG still picture compression standard,” Commun. ACM 34, 31–44 (1991).
[CrossRef]

Walls, B.

B. Walls, A. Mahalanobis, “Performance of the MACH filter and DCCF algorithms in the presence of data compression,” in Automatic Target Recognition IX, F. A. Sadjadi, ed., Proc. SPIE3718, 376–387 (1999).
[CrossRef]

Weaver, A.

L. M. Novak, G. J. Owirka, A. Weaver, “Automatic target recognition using enhanced resolution SAR data,” IEEE Trans. Aerosp. Electron. Syst. 35, 157–175 (1999).
[CrossRef]

Weaver, A. L.

L. M. Novak, G. J. Owirka, W. S. Brower, A. L. Weaver, “The automatic target recognition system in SAIP,” Lincoln Lab. J. 10, 187–202 (1997).

App. Opt. (1)

A. Mahalanobis, B. V. K. Vijaya Kumar, S. R. F. Sims, “Distance-classifier correlation filters for multiclass target recognition,” App. Opt. 35, 3127–3133 (1996).
[CrossRef]

Appl. Opt. (4)

Commun. ACM (1)

G. K. Wallace, “The JPEG still picture compression standard,” Commun. ACM 34, 31–44 (1991).
[CrossRef]

IEEE ASSP Mag. (1)

P. P. Vaidyanathan, “Quadrature mirror filter banks, M-band extensions and perfect reconstruction techniques,” IEEE ASSP Mag. 4, June1987, pp. 4–20.

IEEE Trans. Acoust. Speech and Signal Process. (1)

P. P. Vaidyanathan, “Theory and design of M-channel maximally decimated quadrature mirror filters with arbitrary M, having the perfect reconstruction property,” IEEE Trans. Acoust. Speech and Signal Process. ASSP-35, 476–492 (1987).
[CrossRef]

IEEE Trans. Acoust. Speech Signal Process. (2)

M. A. Vetterli, “A theory of multirate filter banks,” IEEE Trans. Acoust. Speech Signal Process. ASSP-35, 356–372 (1987).
[CrossRef]

V. K. Jain, R. E. Crochiere, “Quadrature mirror filter design in the time domain,” IEEE Trans. Acoust. Speech Signal Process. ASSP-32, 353–360 (1984).
[CrossRef]

IEEE Trans. Aerosp. Electron. Syst. (1)

L. M. Novak, G. J. Owirka, A. Weaver, “Automatic target recognition using enhanced resolution SAR data,” IEEE Trans. Aerosp. Electron. Syst. 35, 157–175 (1999).
[CrossRef]

IEEE Trans. Signal Proc. (1)

S. S. Hemami, R. M. Gray, “Subband filters optimized for lost coefficient reconstruction,” IEEE Trans. Signal Proc. 45, 763–767 (1997).
[CrossRef]

IEEE Trans. Signal Process. (1)

J. M. Shapiro, “Embedded image coding using zerotrees of wavelet coefficients,” IEEE Trans. Signal Process. 41, 3445–3462 (1993).
[CrossRef]

IRE Trans. Inf. Theor. (1)

G. L. Turin, “An introduction to matched filters,” IRE Trans. Inf. Theor. 6, 311–329 (1960).
[CrossRef]

J. Opt. Soc. Am. A (1)

Lincoln Lab. J. (1)

L. M. Novak, G. J. Owirka, W. S. Brower, A. L. Weaver, “The automatic target recognition system in SAIP,” Lincoln Lab. J. 10, 187–202 (1997).

Opt. Eng. (1)

A. Mahalanobis, B. Vijaya Kumar, “Optimality of the maximum average correlation height filter for detection of targets in noise,” Opt. Eng. 36, 2642–2648 (1997).
[CrossRef]

Pattern Recogn. (1)

A. Mahalanobis, A. Forman, N. Day, M. Bower, R. Cherry, “Multiclass SAR ATR using shift-invariant correlation filters,” Pattern Recogn. 27, 619–626 (1994).
[CrossRef]

Proc. IEEE (2)

P. P. Vaidyanathan, “Multirate digital filters, filter banks, polyphase networks, and applications: a tutorial,” Proc. IEEE 78, 56–93 (1990).
[CrossRef]

D. O. North, “An analysis of the factors which determine signal/noise discrimination in pulsed carrier systems,” Proc. IEEE 51, 1016–1027 (1963).
[CrossRef]

Proc. SPIE (1)

K. G. Leib, J. C. Mendelsohn, “Application of the matched filter image correlator to IR, SAR, and visual target data,” in Proc. SPIE 519, 96–105 (1985).
[CrossRef]

Other (25)

W. M. Boerner, A. B. Kostinski, “On the concept of the polarimetric matched filter in high resolution radar imaging,” in Proceedings of the IEEE 1988 International Symposium Digest: Antennas and Propagation (Institute of Electrical and Electronics Engineers, New York, 1988), vol. 2, pp. 533–536.
[CrossRef]

L. M. Novak, G. J. Owirka, W. S. Brower, “Performance of a 20-target MSE classifier,” in Radar Sensor Technology III, R. Trebits, J. L. Kurtz, eds., Proc. SPIE3395, 138–151 (1998).
[CrossRef]

A. Mahalanobis, L. Ortiz, B. V. K. Vijaya Kumar, “Performance of the MACH filter and DCCF algorithms on the 10-class public release MSTAR data set,” in Algorithms for Synthetic Aperture Radar Imagery VI, E. G. Zelnio, ed., Proc. SPIE3721, 285–291 (1999).
[CrossRef]

A. Mahalanobis, L. Ortiz, B. V. K. Vijaya Kumar, A. Ezekial, “Correlation ATR performance using Xpatch (synthetic) training data,” in Algorithms for Synthetic Aperture Radar Imagery VII, E. G. Zelnio, ed., Proc. SPIE4053, 340–344 (2000).
[CrossRef]

K. Sayood, Introduction to Data Compression (Morgan Kaufmann, Los Altos, Calif., 1996).

H. C. Andrews, W. K. Pratt, “Fourier transform coding of images,” in Proceedings of Hawaii International Conference on Systems and Science (U. Hawaii Press, Honolulu, 1968), pp. 677–679.

H. C. Andrews, W. K. Pratt, “Transform image coding,” in Proceedings of the Symposium on Computer Processing in Communications (Wiley-Interscience, Chichester, UK, 1970), pp. 63–84.

A. Croisier, D. Esteban, C. Galand, “Perfect channel splitting by use of interpolation/decimation techniques,” in Proceedings of the First International Conference on Information Sciences and Systems (Hemisphere, Washington, D.C., 1977), pp. 443–446.

D. W. Carlson, “Optimal tradeoff composite correlation filters,” Ph.D. thesis (Carnegie Mellon University, Pittsburgh, Pa., 1996).

C. Daniell, “Object recognition in compressed imagery,” Ph.D. thesis (California Institute of Technology, Pasadena, California, 2000).

C. E. Daniell, A. Mahalanobis, R. Goodman, “Joint optimization of correlation and subband transform filters for the dual requirements of recognition and compression,” submitted to IEEE Trans. Image Process.

P. P. Vaidyanathan, Multirate Systems and Filter Banks (Prentice-Hall, Englewood Cliffs, N.J., 1993).

M. A. Vetterli, J. Kovacevic, Wavelets and Subband Coding (Prentice-Hall, Englewood Cliffs, N.J., 1995).

G. Strang, T. Nguyen, Wavelets and Filter Banks (Wellesley-Cambridge, Wellesley, Mass., 1996).

H. Andrews, Computer Techniques in Image Processing, (Academic, New York, 1970), pp. 55–71.

R. O. Duda, P. E. Hart, Pattern Classification and Scene Analysis, (Wiley-Interscience, New York, 1973), pp. 276–284.

W. K. Pratt, Digital Image Processing, (Wiley, New York, 1978), Parts 5 and 6.

B. V. K. Vijaya Kumar, A. Mahalanobis, R. Juday, Correlation Pattern Recognition (Cambridge U. Press, Cambridge) (to be published).

A. Mahalanobis, “New correlation filters for symbolic and rule based pattern recognition,” Ph.D. thesis (Carnegie Mellon University, Pittsburgh, Pa., 1987).

A. Mahalanobis, B. V. K. Vijaya Kumar, “Polynomial filters for higher order correlation and multi-input information fusion,” in Optoelectronic Information Processing (SPIE, Bellingham, Wash., 1997), pp. 221–231.

B. Walls, A. Mahalanobis, “Performance of the MACH filter and DCCF algorithms in the presence of data compression,” in Automatic Target Recognition IX, F. A. Sadjadi, ed., Proc. SPIE3718, 376–387 (1999).
[CrossRef]

T-C. Liu, S. Mitra, “Fingerprint recognition of wavelet-based compressed images by neuro-fuzzy clustering,” in Applications of Fuzzy Logic Technology III, B. Bosacchi, J. C. Bezdek, eds., Proc. SPIE2761, 76–86 (1996).
[CrossRef]

F. B. Shin, D. H. Kil, “Integrated approach to bandwidth reduction and mine detection in shallow water with reduced-dimension image compression and automatic target recognition algorithms,” in Detection and Remediation Technologies for Mines and Minelike Targets II, A. C. Dubey, R. L. Barnard, eds., Proc. SPIE3079, 203–212 (1997).
[CrossRef]

S. S. Hemami, “Reconstruction of compressed images and video for lossy packet networks,” Ph.D. thesis (Stanford University, Stanford, Calif., 1995).

L. Hostetler, “Template based ATR,” presented at the Fifty-Second Automatic Target Recognizer Working Group, Space and Naval Warfare Systems Center San Diego, San Diego, Calif., 8 June 1999.

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Figures (10)

Fig. 1
Fig. 1

SCF architecture. The PCF provides a way to simultaneously correlate all subbands. Each subband takes its own input channel and thus its own PCF. We use an analogous structure for each level of the subband decomposition.

Fig. 2
Fig. 2

Downsampling effect on the LL subband patterns at level one. The subband downsampling process is not shift invariant. We illustrate the issue for the LL subband only, although the upper-band coefficients are most affected. Note that the four LL images shown are not simply shifted versions of one another. Rather, they result from shifts of the original image. On close inspection, one can see that the LL subband of each branch exhibits a different pattern. Furthermore, the problem explodes at the lower decomposition levels, as each shifted version on level n spawns four shift variations on level n - 1.

Fig. 3
Fig. 3

Partial tetradic training tree for training over image shifts. Only the LL subband of the original placement spawns four shifting branches. Thus there are 4N training images per subband of each level, given N images in the original training set. This is the training method we employ in this paper.

Fig. 4
Fig. 4

Shift-tolerant QMF time-domain response. The shift-tolerant QMF values are quite different from our baseline QMF. The three values at its center are reduced, whereas the remaining sidelobes increase in magnitude.

Fig. 5
Fig. 5

Shift-tolerant QMF frequency response. The shift-tolerant QMF (solid curve) forms a stop band, as it tries to attenuate the frequencies that most affect the shifted response. The baseline QMF frequency is shown as a dashed curve for comparison.

Fig. 6
Fig. 6

Probability of correct classification versus bit rate.

Fig. 7
Fig. 7

Probability of error in classification versus bit rate.

Fig. 8
Fig. 8

Probability of rejection versus bit rate.

Fig. 9
Fig. 9

PSN ratio versus bit rate.

Fig. 10
Fig. 10

Reconstructed image MSE versus bit rate.

Tables (6)

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Table 1 Performance Metrics of the Baseline and Shift-Tolerant QMFa

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Table 2 Recognition Metrics for the Four SCF Systemsa

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Table 3 Reconstruction Metrics for the Four SCF Systemsa

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Table 4 Multiresolution Performance of Baseline and Shift-Tolerant QMFa

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Table 5 Subbands Dropped at Low Bit Rates

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Table 6 Multiresolution Performance Summary of SCF Systema

Equations (17)

Equations on this page are rendered with MathJax. Learn more.

ym, n=p=1P cpm, nxpm, n,
ym, n=-1p=1P Cp*k, lXpk, l,
Mpk, l=1Ni=1N Xpik, l,
Dpqk, l=1Ni=1N Xpik, lXqi*k, l
Spqk, l=Dpqk, l-Mpk, lMq*k, l.
Bpqk, l=αSpqk, l+βDpqk, l+γμpq,
c1c2=B11B12B21B22-1m1m2,
f=ijTTTij2+h0Th0-12+n=1Nf h0n-22,
h1n=-1nh0n, g0n=h0n, g1n=-h1n.
dm, n=xm, n-xm-1, n-1m, n,
Rd=EddT.
Φ=h0TRdh0.
Φ=h0TRdh0+h1.
Θ=h1TRxh1,
Rx=ExxT.
ds=λΦ+1-λΘ.
f=ijTTTij2+h0Th0-12+n=1Nf h0n-22+0.2λh0TRdh0+h1+1-λh1TRxh1,

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