Abstract

Many applications require detection of multiple features that locally remain consistent in shape and intensity characteristics, but may globally change position with respect to one another over time or under different circumstances. We refer to these feature sets, defined by their characteristic relative positioning, as multifeature constellations. We introduce a method of processing in which multiple levels of correlation, using specially designed composite feature detection filters, are used to first detect local features, and then to detect constellations of these local features. We include experimental procedures and results indicating how the use of multifeature constellation detection may be utilized in applications such as sign language recognition and fingerprint matching.

© 2011 Optical Society of America

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  1. W. Holzapfel and M. Sofsky, “Evaluation of correlation methods applying neural networks,” Neural Comput. Applic. 12, 26–32 (2003).
    [CrossRef]
  2. B. B. Spratling IV and D. Mortari, “A survey on star identification algorithms,” Algorithms 2, 93–107 (2009).
    [CrossRef]
  3. C. Padgett and K. K. A. Delgado, “Grid algorithm for autonomous star identification,” IEEE Trans. Aerosp. Electron. Syst. 33, 202–213 (1997).
    [CrossRef]
  4. M. E. Lhamon, L. G. Hassebrook, and J. P. Chatterjee, “Complex spatial images for multi-parameter distortion-invariant optical pattern recognition and high level morphological transformations,” Proc. SPIE 2752, 23–30 (1996).
    [CrossRef]
  5. R. W. Cohn and L. G. Hassebrook, “Representations of fully complex functions on real-time spatial light modulators,” in Optical Information Processing, F.T. S.Yu and S.Jutamulia, eds. (Cambridge Univ. Press, 1998).
  6. W. Su and L. G. Hassebrook, “Pose and position tracking with super image vector inner products,” Appl. Opt. 45, 8083–8091(2006).
    [CrossRef] [PubMed]
  7. A. Vander Lugt, “Signal detection by complex spatial filtering,” IEEE Trans. Inf. Theory 10, 139–145 (1964).
    [CrossRef]
  8. B. V. K. Vijaya Kumar, “Tutorial survey of composite filter designs for optical correlators,” Appl. Opt. 31, 4773–4801 (1992).
    [CrossRef]
  9. G. Ravichandran and D. Casasent, “Minimum noise and correlation energy optical correlation filter,” Appl. Opt. 31, 1823–1833 (1992).
    [CrossRef] [PubMed]
  10. A. Mahalanobis, B. V. K. Vijaya Kumar, S. Song, S. R. F. Sims, and J. F. Epperson, “Unconstrained correlation filters,” Appl. Opt. 33, 3751–3759 (1994).
    [CrossRef] [PubMed]
  11. G.-D. Guo and C. Dyer, “Patch-based image correlation with rapid filtering,” in IEEE Conference on Computer Vision and Pattern Recognition, 2007, CVPR’07 (IEEE, 2007).
  12. W. Li and Y.-X. He, “Face detection based on QFT phase-only Correlation template match,” in 2009 1st International Conference on Information Science and Engineering (ICISE) (IEEE, 2009), pp. 1231–1234.
    [CrossRef]
  13. M. Rahmati and L. G. Hassebrook, “Intensity- and distortion-invariant pattern recognition with complex linear morphology,” Pattern Recognit. 27, 549–568 (1994).
    [CrossRef]
  14. M. Rahmati, L. G. Hassebrook, and B. V. K. Vijaya Kumar, “Automatic target recognition with intensity- and distortion-invariant hybrid composite filters,” Proc. SPIE 1959, 133–145 (1993).
    [CrossRef]
  15. J. M. Coggins and A. K. Jain, “A spatial-filtering approach to texture analysis,” Pattern Recognit. Lett. 3, 195–203(1985).
    [CrossRef]
  16. C. J. Casey, L. G. Hassebrook, and D. L. Lau, “Structured light illumination methods for continuous motion hand and face-computer interaction,” in Human-Computer Interaction, New Developments, International Journal of Advanced Robotic System, K.Asai, ed. (In-Teh, 2008), pp. 297–308.
  17. L. G. Hassebrook, “Composite correlation filter for O-ring detection in stationary colored noise,” Proc. SPIE 7340, 734007(2009).
    [CrossRef]
  18. H. L. Van Trees, Detection, Estimation, and Modulation Theory, Part I (Wiley, 1968).
  19. J. Ravikiran, K. Mahesh, S. Mahishi, R. Dheeraj, S. Sudheender, and N. V. Pujari, “Finger detection for sign language recognition,” in Proceedings of the International Conference on Computer Science, International Multi-Conference of Engineers and Computer Scientists—IMECS 2009 ICCS-2009 (International Association of Engineers, 2009), pp. 489–493.
  20. N. Shimada, Y. Shirai, Y. Kuno, and J. Miura, “Hand gesture estimation and model refinement using monocular camera—ambiguity limitation by inequality constraints,” in Proceedings of the 3rd IEEE International Conference on Automatic Face and Gesture Recognition (IEEE, 1998), pp. 268–273.
    [CrossRef]
  21. T. Starner and A. Pentland, “Real-time American Sign Language recognition from video using hidden Markov models,” in Proceedings of the IEEE International Symposium on Computer Vision (IEEE, 1995), pp. 265–270.
    [CrossRef]
  22. D. D. Nguyen, T. C. Pham, X. D. Pham, S. H. Jin, and J. W. Jeon, “Finger extraction from scene with grayscale morphology and BLOB analysis.,” in Proceedings of the 2008 IEEE International Conference on Robotics and Biomimetics (ROBIO’09) (IEEE, 2009), pp. 324–329.
    [CrossRef]
  23. K. Imagawa, S. Lu, and S. Igi, “Color-based hands tracking system for sign language recognition,” in Proceedings of the Third IEEE International Conference on Automatic Face and Gesture Recognition (IEEE, 1998), pp. 462–467.
    [CrossRef]
  24. V. Yalla and L. G. Hassebrook, “Very-high resolution 3D surface scanning using multi-frequency phase measuring profilometry,” Proc. SPIE 5798, 44–53(2005).
    [CrossRef]
  25. Y. Wang, L. G. Hassebrook and D. L. Lau, “Data acquisition and processing of 3-D Fingerprints,” IEEE Trans. Inf. Forensics Secur. 5, 750–760 (2010).
    [CrossRef]

2010

Y. Wang, L. G. Hassebrook and D. L. Lau, “Data acquisition and processing of 3-D Fingerprints,” IEEE Trans. Inf. Forensics Secur. 5, 750–760 (2010).
[CrossRef]

2009

L. G. Hassebrook, “Composite correlation filter for O-ring detection in stationary colored noise,” Proc. SPIE 7340, 734007(2009).
[CrossRef]

B. B. Spratling IV and D. Mortari, “A survey on star identification algorithms,” Algorithms 2, 93–107 (2009).
[CrossRef]

2006

2005

V. Yalla and L. G. Hassebrook, “Very-high resolution 3D surface scanning using multi-frequency phase measuring profilometry,” Proc. SPIE 5798, 44–53(2005).
[CrossRef]

2003

W. Holzapfel and M. Sofsky, “Evaluation of correlation methods applying neural networks,” Neural Comput. Applic. 12, 26–32 (2003).
[CrossRef]

1997

C. Padgett and K. K. A. Delgado, “Grid algorithm for autonomous star identification,” IEEE Trans. Aerosp. Electron. Syst. 33, 202–213 (1997).
[CrossRef]

1996

M. E. Lhamon, L. G. Hassebrook, and J. P. Chatterjee, “Complex spatial images for multi-parameter distortion-invariant optical pattern recognition and high level morphological transformations,” Proc. SPIE 2752, 23–30 (1996).
[CrossRef]

1994

A. Mahalanobis, B. V. K. Vijaya Kumar, S. Song, S. R. F. Sims, and J. F. Epperson, “Unconstrained correlation filters,” Appl. Opt. 33, 3751–3759 (1994).
[CrossRef] [PubMed]

M. Rahmati and L. G. Hassebrook, “Intensity- and distortion-invariant pattern recognition with complex linear morphology,” Pattern Recognit. 27, 549–568 (1994).
[CrossRef]

1993

M. Rahmati, L. G. Hassebrook, and B. V. K. Vijaya Kumar, “Automatic target recognition with intensity- and distortion-invariant hybrid composite filters,” Proc. SPIE 1959, 133–145 (1993).
[CrossRef]

1992

1985

J. M. Coggins and A. K. Jain, “A spatial-filtering approach to texture analysis,” Pattern Recognit. Lett. 3, 195–203(1985).
[CrossRef]

1964

A. Vander Lugt, “Signal detection by complex spatial filtering,” IEEE Trans. Inf. Theory 10, 139–145 (1964).
[CrossRef]

Casasent, D.

Casey, C. J.

C. J. Casey, L. G. Hassebrook, and D. L. Lau, “Structured light illumination methods for continuous motion hand and face-computer interaction,” in Human-Computer Interaction, New Developments, International Journal of Advanced Robotic System, K.Asai, ed. (In-Teh, 2008), pp. 297–308.

Chatterjee, J. P.

M. E. Lhamon, L. G. Hassebrook, and J. P. Chatterjee, “Complex spatial images for multi-parameter distortion-invariant optical pattern recognition and high level morphological transformations,” Proc. SPIE 2752, 23–30 (1996).
[CrossRef]

Coggins, J. M.

J. M. Coggins and A. K. Jain, “A spatial-filtering approach to texture analysis,” Pattern Recognit. Lett. 3, 195–203(1985).
[CrossRef]

Cohn, R. W.

R. W. Cohn and L. G. Hassebrook, “Representations of fully complex functions on real-time spatial light modulators,” in Optical Information Processing, F.T. S.Yu and S.Jutamulia, eds. (Cambridge Univ. Press, 1998).

Delgado, K. K. A.

C. Padgett and K. K. A. Delgado, “Grid algorithm for autonomous star identification,” IEEE Trans. Aerosp. Electron. Syst. 33, 202–213 (1997).
[CrossRef]

Dheeraj, R.

J. Ravikiran, K. Mahesh, S. Mahishi, R. Dheeraj, S. Sudheender, and N. V. Pujari, “Finger detection for sign language recognition,” in Proceedings of the International Conference on Computer Science, International Multi-Conference of Engineers and Computer Scientists—IMECS 2009 ICCS-2009 (International Association of Engineers, 2009), pp. 489–493.

Dyer, C.

G.-D. Guo and C. Dyer, “Patch-based image correlation with rapid filtering,” in IEEE Conference on Computer Vision and Pattern Recognition, 2007, CVPR’07 (IEEE, 2007).

Epperson, J. F.

Guo, G.-D.

G.-D. Guo and C. Dyer, “Patch-based image correlation with rapid filtering,” in IEEE Conference on Computer Vision and Pattern Recognition, 2007, CVPR’07 (IEEE, 2007).

Hassebrook, L. G.

Y. Wang, L. G. Hassebrook and D. L. Lau, “Data acquisition and processing of 3-D Fingerprints,” IEEE Trans. Inf. Forensics Secur. 5, 750–760 (2010).
[CrossRef]

L. G. Hassebrook, “Composite correlation filter for O-ring detection in stationary colored noise,” Proc. SPIE 7340, 734007(2009).
[CrossRef]

W. Su and L. G. Hassebrook, “Pose and position tracking with super image vector inner products,” Appl. Opt. 45, 8083–8091(2006).
[CrossRef] [PubMed]

V. Yalla and L. G. Hassebrook, “Very-high resolution 3D surface scanning using multi-frequency phase measuring profilometry,” Proc. SPIE 5798, 44–53(2005).
[CrossRef]

M. E. Lhamon, L. G. Hassebrook, and J. P. Chatterjee, “Complex spatial images for multi-parameter distortion-invariant optical pattern recognition and high level morphological transformations,” Proc. SPIE 2752, 23–30 (1996).
[CrossRef]

M. Rahmati and L. G. Hassebrook, “Intensity- and distortion-invariant pattern recognition with complex linear morphology,” Pattern Recognit. 27, 549–568 (1994).
[CrossRef]

M. Rahmati, L. G. Hassebrook, and B. V. K. Vijaya Kumar, “Automatic target recognition with intensity- and distortion-invariant hybrid composite filters,” Proc. SPIE 1959, 133–145 (1993).
[CrossRef]

C. J. Casey, L. G. Hassebrook, and D. L. Lau, “Structured light illumination methods for continuous motion hand and face-computer interaction,” in Human-Computer Interaction, New Developments, International Journal of Advanced Robotic System, K.Asai, ed. (In-Teh, 2008), pp. 297–308.

R. W. Cohn and L. G. Hassebrook, “Representations of fully complex functions on real-time spatial light modulators,” in Optical Information Processing, F.T. S.Yu and S.Jutamulia, eds. (Cambridge Univ. Press, 1998).

He, Y.-X.

W. Li and Y.-X. He, “Face detection based on QFT phase-only Correlation template match,” in 2009 1st International Conference on Information Science and Engineering (ICISE) (IEEE, 2009), pp. 1231–1234.
[CrossRef]

Holzapfel, W.

W. Holzapfel and M. Sofsky, “Evaluation of correlation methods applying neural networks,” Neural Comput. Applic. 12, 26–32 (2003).
[CrossRef]

Igi, S.

K. Imagawa, S. Lu, and S. Igi, “Color-based hands tracking system for sign language recognition,” in Proceedings of the Third IEEE International Conference on Automatic Face and Gesture Recognition (IEEE, 1998), pp. 462–467.
[CrossRef]

Imagawa, K.

K. Imagawa, S. Lu, and S. Igi, “Color-based hands tracking system for sign language recognition,” in Proceedings of the Third IEEE International Conference on Automatic Face and Gesture Recognition (IEEE, 1998), pp. 462–467.
[CrossRef]

Jain, A. K.

J. M. Coggins and A. K. Jain, “A spatial-filtering approach to texture analysis,” Pattern Recognit. Lett. 3, 195–203(1985).
[CrossRef]

Jeon, J. W.

D. D. Nguyen, T. C. Pham, X. D. Pham, S. H. Jin, and J. W. Jeon, “Finger extraction from scene with grayscale morphology and BLOB analysis.,” in Proceedings of the 2008 IEEE International Conference on Robotics and Biomimetics (ROBIO’09) (IEEE, 2009), pp. 324–329.
[CrossRef]

Jin, S. H.

D. D. Nguyen, T. C. Pham, X. D. Pham, S. H. Jin, and J. W. Jeon, “Finger extraction from scene with grayscale morphology and BLOB analysis.,” in Proceedings of the 2008 IEEE International Conference on Robotics and Biomimetics (ROBIO’09) (IEEE, 2009), pp. 324–329.
[CrossRef]

Kuno, Y.

N. Shimada, Y. Shirai, Y. Kuno, and J. Miura, “Hand gesture estimation and model refinement using monocular camera—ambiguity limitation by inequality constraints,” in Proceedings of the 3rd IEEE International Conference on Automatic Face and Gesture Recognition (IEEE, 1998), pp. 268–273.
[CrossRef]

Lau, D. L.

Y. Wang, L. G. Hassebrook and D. L. Lau, “Data acquisition and processing of 3-D Fingerprints,” IEEE Trans. Inf. Forensics Secur. 5, 750–760 (2010).
[CrossRef]

C. J. Casey, L. G. Hassebrook, and D. L. Lau, “Structured light illumination methods for continuous motion hand and face-computer interaction,” in Human-Computer Interaction, New Developments, International Journal of Advanced Robotic System, K.Asai, ed. (In-Teh, 2008), pp. 297–308.

Lhamon, M. E.

M. E. Lhamon, L. G. Hassebrook, and J. P. Chatterjee, “Complex spatial images for multi-parameter distortion-invariant optical pattern recognition and high level morphological transformations,” Proc. SPIE 2752, 23–30 (1996).
[CrossRef]

Li, W.

W. Li and Y.-X. He, “Face detection based on QFT phase-only Correlation template match,” in 2009 1st International Conference on Information Science and Engineering (ICISE) (IEEE, 2009), pp. 1231–1234.
[CrossRef]

Lu, S.

K. Imagawa, S. Lu, and S. Igi, “Color-based hands tracking system for sign language recognition,” in Proceedings of the Third IEEE International Conference on Automatic Face and Gesture Recognition (IEEE, 1998), pp. 462–467.
[CrossRef]

Mahalanobis, A.

Mahesh, K.

J. Ravikiran, K. Mahesh, S. Mahishi, R. Dheeraj, S. Sudheender, and N. V. Pujari, “Finger detection for sign language recognition,” in Proceedings of the International Conference on Computer Science, International Multi-Conference of Engineers and Computer Scientists—IMECS 2009 ICCS-2009 (International Association of Engineers, 2009), pp. 489–493.

Mahishi, S.

J. Ravikiran, K. Mahesh, S. Mahishi, R. Dheeraj, S. Sudheender, and N. V. Pujari, “Finger detection for sign language recognition,” in Proceedings of the International Conference on Computer Science, International Multi-Conference of Engineers and Computer Scientists—IMECS 2009 ICCS-2009 (International Association of Engineers, 2009), pp. 489–493.

Miura, J.

N. Shimada, Y. Shirai, Y. Kuno, and J. Miura, “Hand gesture estimation and model refinement using monocular camera—ambiguity limitation by inequality constraints,” in Proceedings of the 3rd IEEE International Conference on Automatic Face and Gesture Recognition (IEEE, 1998), pp. 268–273.
[CrossRef]

Mortari, D.

B. B. Spratling IV and D. Mortari, “A survey on star identification algorithms,” Algorithms 2, 93–107 (2009).
[CrossRef]

Nguyen, D. D.

D. D. Nguyen, T. C. Pham, X. D. Pham, S. H. Jin, and J. W. Jeon, “Finger extraction from scene with grayscale morphology and BLOB analysis.,” in Proceedings of the 2008 IEEE International Conference on Robotics and Biomimetics (ROBIO’09) (IEEE, 2009), pp. 324–329.
[CrossRef]

Padgett, C.

C. Padgett and K. K. A. Delgado, “Grid algorithm for autonomous star identification,” IEEE Trans. Aerosp. Electron. Syst. 33, 202–213 (1997).
[CrossRef]

Pentland, A.

T. Starner and A. Pentland, “Real-time American Sign Language recognition from video using hidden Markov models,” in Proceedings of the IEEE International Symposium on Computer Vision (IEEE, 1995), pp. 265–270.
[CrossRef]

Pham, T. C.

D. D. Nguyen, T. C. Pham, X. D. Pham, S. H. Jin, and J. W. Jeon, “Finger extraction from scene with grayscale morphology and BLOB analysis.,” in Proceedings of the 2008 IEEE International Conference on Robotics and Biomimetics (ROBIO’09) (IEEE, 2009), pp. 324–329.
[CrossRef]

Pham, X. D.

D. D. Nguyen, T. C. Pham, X. D. Pham, S. H. Jin, and J. W. Jeon, “Finger extraction from scene with grayscale morphology and BLOB analysis.,” in Proceedings of the 2008 IEEE International Conference on Robotics and Biomimetics (ROBIO’09) (IEEE, 2009), pp. 324–329.
[CrossRef]

Pujari, N. V.

J. Ravikiran, K. Mahesh, S. Mahishi, R. Dheeraj, S. Sudheender, and N. V. Pujari, “Finger detection for sign language recognition,” in Proceedings of the International Conference on Computer Science, International Multi-Conference of Engineers and Computer Scientists—IMECS 2009 ICCS-2009 (International Association of Engineers, 2009), pp. 489–493.

Rahmati, M.

M. Rahmati and L. G. Hassebrook, “Intensity- and distortion-invariant pattern recognition with complex linear morphology,” Pattern Recognit. 27, 549–568 (1994).
[CrossRef]

M. Rahmati, L. G. Hassebrook, and B. V. K. Vijaya Kumar, “Automatic target recognition with intensity- and distortion-invariant hybrid composite filters,” Proc. SPIE 1959, 133–145 (1993).
[CrossRef]

Ravichandran, G.

Ravikiran, J.

J. Ravikiran, K. Mahesh, S. Mahishi, R. Dheeraj, S. Sudheender, and N. V. Pujari, “Finger detection for sign language recognition,” in Proceedings of the International Conference on Computer Science, International Multi-Conference of Engineers and Computer Scientists—IMECS 2009 ICCS-2009 (International Association of Engineers, 2009), pp. 489–493.

Shimada, N.

N. Shimada, Y. Shirai, Y. Kuno, and J. Miura, “Hand gesture estimation and model refinement using monocular camera—ambiguity limitation by inequality constraints,” in Proceedings of the 3rd IEEE International Conference on Automatic Face and Gesture Recognition (IEEE, 1998), pp. 268–273.
[CrossRef]

Shirai, Y.

N. Shimada, Y. Shirai, Y. Kuno, and J. Miura, “Hand gesture estimation and model refinement using monocular camera—ambiguity limitation by inequality constraints,” in Proceedings of the 3rd IEEE International Conference on Automatic Face and Gesture Recognition (IEEE, 1998), pp. 268–273.
[CrossRef]

Sims, S. R. F.

Sofsky, M.

W. Holzapfel and M. Sofsky, “Evaluation of correlation methods applying neural networks,” Neural Comput. Applic. 12, 26–32 (2003).
[CrossRef]

Song, S.

Spratling, B. B.

B. B. Spratling IV and D. Mortari, “A survey on star identification algorithms,” Algorithms 2, 93–107 (2009).
[CrossRef]

Starner, T.

T. Starner and A. Pentland, “Real-time American Sign Language recognition from video using hidden Markov models,” in Proceedings of the IEEE International Symposium on Computer Vision (IEEE, 1995), pp. 265–270.
[CrossRef]

Su, W.

Sudheender, S.

J. Ravikiran, K. Mahesh, S. Mahishi, R. Dheeraj, S. Sudheender, and N. V. Pujari, “Finger detection for sign language recognition,” in Proceedings of the International Conference on Computer Science, International Multi-Conference of Engineers and Computer Scientists—IMECS 2009 ICCS-2009 (International Association of Engineers, 2009), pp. 489–493.

Van Trees, H. L.

H. L. Van Trees, Detection, Estimation, and Modulation Theory, Part I (Wiley, 1968).

Vander Lugt, A.

A. Vander Lugt, “Signal detection by complex spatial filtering,” IEEE Trans. Inf. Theory 10, 139–145 (1964).
[CrossRef]

Vijaya Kumar, B. V. K.

Wang, Y.

Y. Wang, L. G. Hassebrook and D. L. Lau, “Data acquisition and processing of 3-D Fingerprints,” IEEE Trans. Inf. Forensics Secur. 5, 750–760 (2010).
[CrossRef]

Yalla, V.

V. Yalla and L. G. Hassebrook, “Very-high resolution 3D surface scanning using multi-frequency phase measuring profilometry,” Proc. SPIE 5798, 44–53(2005).
[CrossRef]

Algorithms

B. B. Spratling IV and D. Mortari, “A survey on star identification algorithms,” Algorithms 2, 93–107 (2009).
[CrossRef]

Appl. Opt.

IEEE Trans. Aerosp. Electron. Syst.

C. Padgett and K. K. A. Delgado, “Grid algorithm for autonomous star identification,” IEEE Trans. Aerosp. Electron. Syst. 33, 202–213 (1997).
[CrossRef]

IEEE Trans. Inf. Forensics Secur.

Y. Wang, L. G. Hassebrook and D. L. Lau, “Data acquisition and processing of 3-D Fingerprints,” IEEE Trans. Inf. Forensics Secur. 5, 750–760 (2010).
[CrossRef]

IEEE Trans. Inf. Theory

A. Vander Lugt, “Signal detection by complex spatial filtering,” IEEE Trans. Inf. Theory 10, 139–145 (1964).
[CrossRef]

Neural Comput. Applic.

W. Holzapfel and M. Sofsky, “Evaluation of correlation methods applying neural networks,” Neural Comput. Applic. 12, 26–32 (2003).
[CrossRef]

Pattern Recognit.

M. Rahmati and L. G. Hassebrook, “Intensity- and distortion-invariant pattern recognition with complex linear morphology,” Pattern Recognit. 27, 549–568 (1994).
[CrossRef]

Pattern Recognit. Lett.

J. M. Coggins and A. K. Jain, “A spatial-filtering approach to texture analysis,” Pattern Recognit. Lett. 3, 195–203(1985).
[CrossRef]

Proc. SPIE

V. Yalla and L. G. Hassebrook, “Very-high resolution 3D surface scanning using multi-frequency phase measuring profilometry,” Proc. SPIE 5798, 44–53(2005).
[CrossRef]

M. Rahmati, L. G. Hassebrook, and B. V. K. Vijaya Kumar, “Automatic target recognition with intensity- and distortion-invariant hybrid composite filters,” Proc. SPIE 1959, 133–145 (1993).
[CrossRef]

L. G. Hassebrook, “Composite correlation filter for O-ring detection in stationary colored noise,” Proc. SPIE 7340, 734007(2009).
[CrossRef]

M. E. Lhamon, L. G. Hassebrook, and J. P. Chatterjee, “Complex spatial images for multi-parameter distortion-invariant optical pattern recognition and high level morphological transformations,” Proc. SPIE 2752, 23–30 (1996).
[CrossRef]

Other

R. W. Cohn and L. G. Hassebrook, “Representations of fully complex functions on real-time spatial light modulators,” in Optical Information Processing, F.T. S.Yu and S.Jutamulia, eds. (Cambridge Univ. Press, 1998).

G.-D. Guo and C. Dyer, “Patch-based image correlation with rapid filtering,” in IEEE Conference on Computer Vision and Pattern Recognition, 2007, CVPR’07 (IEEE, 2007).

W. Li and Y.-X. He, “Face detection based on QFT phase-only Correlation template match,” in 2009 1st International Conference on Information Science and Engineering (ICISE) (IEEE, 2009), pp. 1231–1234.
[CrossRef]

H. L. Van Trees, Detection, Estimation, and Modulation Theory, Part I (Wiley, 1968).

J. Ravikiran, K. Mahesh, S. Mahishi, R. Dheeraj, S. Sudheender, and N. V. Pujari, “Finger detection for sign language recognition,” in Proceedings of the International Conference on Computer Science, International Multi-Conference of Engineers and Computer Scientists—IMECS 2009 ICCS-2009 (International Association of Engineers, 2009), pp. 489–493.

N. Shimada, Y. Shirai, Y. Kuno, and J. Miura, “Hand gesture estimation and model refinement using monocular camera—ambiguity limitation by inequality constraints,” in Proceedings of the 3rd IEEE International Conference on Automatic Face and Gesture Recognition (IEEE, 1998), pp. 268–273.
[CrossRef]

T. Starner and A. Pentland, “Real-time American Sign Language recognition from video using hidden Markov models,” in Proceedings of the IEEE International Symposium on Computer Vision (IEEE, 1995), pp. 265–270.
[CrossRef]

D. D. Nguyen, T. C. Pham, X. D. Pham, S. H. Jin, and J. W. Jeon, “Finger extraction from scene with grayscale morphology and BLOB analysis.,” in Proceedings of the 2008 IEEE International Conference on Robotics and Biomimetics (ROBIO’09) (IEEE, 2009), pp. 324–329.
[CrossRef]

K. Imagawa, S. Lu, and S. Igi, “Color-based hands tracking system for sign language recognition,” in Proceedings of the Third IEEE International Conference on Automatic Face and Gesture Recognition (IEEE, 1998), pp. 462–467.
[CrossRef]

C. J. Casey, L. G. Hassebrook, and D. L. Lau, “Structured light illumination methods for continuous motion hand and face-computer interaction,” in Human-Computer Interaction, New Developments, International Journal of Advanced Robotic System, K.Asai, ed. (In-Teh, 2008), pp. 297–308.

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Figures (12)

Fig. 1
Fig. 1

Constellation processing flowchart.

Fig. 2
Fig. 2

(a) Component feature replicated into the ideal signal; (b) randomly shifted component features signal.

Fig. 3
Fig. 3

(a) First level correlation result; (b) isolated peak location pulses; (c) constellation filter waveform; (d) shifted versus unshifted constellation responses; (e) responses for Level 1 versus traditional single signal correlation.

Fig. 4
Fig. 4

Identification results, traditional correlation versus constellation.

Fig. 5
Fig. 5

(a) Sign letter L and (b) the letter Y. (c) Three different ring filters used to detect fingertip and knuckle features.

Fig. 6
Fig. 6

(a) Overlay of Level 2 constellation filter for L sign, and (b) Level 2 constellation filter overlay for Y sign.

Fig. 7
Fig. 7

Sign language Level 2 peak values, matched versus nonmatched.

Fig. 8
Fig. 8

(a) Bipolar fingerprint. (b) Level 1 fingerprint feature tiles.

Fig. 9
Fig. 9

(a) Superimposed correlation peak locations indicate skin stretching distribution and are used in determining the size and shape of the (b) constellation filter.

Fig. 10
Fig. 10

Correlation peak values of both the (a) matching and (b) nonmatching fingerprints. The dashed black line signifies a decision bound at a peak value of 0.25.

Fig. 11
Fig. 11

PSR of both the (a) matching and (b) nonmatching fingerprints. The dashed black line signifies a decision bound at a PSR of 1.20.

Fig. 12
Fig. 12

Level 2 correlation peak value versus the PSR of both matching and nonmatching fingerprints. Dark dashed line represents a decision boundary that a linear discriminant would optimally define.

Equations (12)

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

s ( t ) = n = 0 N 1 f ( t ) * δ ( t n T ) ,
s ˜ ( t ) = n = 0 N = 1 f ( t ) * δ ( t n T τ ˜ [ n ] ) ,
h b ( t ) = n = 0 N 1 Π ( t W ) * δ ( t n T ) .
h b ( t ) = Π ( t t 0 W ) + Π ( t t 1 W ) Π ( t t m 1 W ) = i = 0 m 1 Π ( t t i W ) .
g ( t ) = f ( t ) s ( t ) = δ ( t t 0 ) + δ ( t t 1 ) δ ( t t m 1 ) = i = 0 m 1 δ ( t t i ) ,
g ˜ ( t ) = f ( t ) s ˜ ( t ) = i = 0 m 1 δ ( t t i ω ˜ ( i ) ) ,
P miss , i = 2 W 2 1 2 π σ 2 exp ( t 2 2 σ 2 ) d t ,
P miss , i = 2 π W 2 σ 2 exp ( u 2 ) d u = erfc ( W 2 σ 2 ) .
P hit , i = erf ( W 2 σ 2 ) .
P hit = P hit , 0 P hit , 1 P hit , m 1 .
P hit = P hit , 0 m .
P e = 1 P hit = 1 P hit , 0 m = [ 1 erf ( W 2 σ 2 ) ] m = [ erfc ( W 2 σ 2 ) ] m .

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