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Applied Optics

Applied Optics


  • Editor: Joseph N. Mait
  • Vol. 48, Iss. 28 — Oct. 1, 2009
  • pp: 5225–5239

Adaptive feature-specific imaging for recognition of non-Gaussian classes

Pawan K. Baheti, Jun Ke, and Mark A. Neifeld  »View Author Affiliations

Applied Optics, Vol. 48, Issue 28, pp. 5225-5239 (2009)

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We present an adaptive feature-specific imaging (AFSI) system for application to an M-class recognition task. The proposed system uses nearest-neighbor-based density estimation to compute the (non- Gaussian) class-conditional densities. We refine the density estimates based on the training data and the knowledge from previous measurements at each step. The projection basis for the AFSI system is also adapted based on the previous measurements at each step. The decision-making process is based on sequential hypothesis testing. We quantify the number of measurements required to achieve a specified probability of error ( P e ) and we compare the AFSI system with an adaptive-conventional (ACONV) system. The AFSI system exhibits significant improvement compared to the ACONV system at low signal-to-noise ratio (SNR), and it is shown that, for an M = 4 hypotheses, SNR = 10 dB , and a desired P e = 10 2 , the AFSI system requires 30 times fewer measurements than the ACONV system. Experimental results validating the AFSI system are presented.

© 2009 Optical Society of America

OCIS Codes
(100.5010) Image processing : Pattern recognition
(110.2970) Imaging systems : Image detection systems
(110.1085) Imaging systems : Adaptive imaging
(110.1758) Imaging systems : Computational imaging

ToC Category:
Imaging Systems

Original Manuscript: April 6, 2009
Revised Manuscript: August 7, 2009
Manuscript Accepted: August 12, 2009
Published: September 21, 2009

Pawan K. Baheti, Jun Ke, and Mark A. Neifeld, "Adaptive feature-specific imaging for recognition of non-Gaussian classes," Appl. Opt. 48, 5225-5239 (2009)

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  1. M. A. Turk and A. P. Pentland, “Face recognition using eigenfaces,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 1991), pp. 586-591. [CrossRef]
  2. P. Belhumeur, J. Hespanha, and D. Kriegman, “Eigenfaces vs. Fisherfaces: recognition using class specific linear projection,” IEEE Trans. Pattern Anal. Mach. Intell. 19, 711-720(1997). [CrossRef]
  3. W. Zhao, R. Chellapa, P. J. Phillips, and A. Rosenfeld, “Face recognition: a literature survey,” ACM Comput. Surv. 35, 399-458 (2003). [CrossRef]
  4. H. H. Barrett, T. Gooley, K. Girodias, J. Rolland, T. White, and J. Lao, “Linear discriminants and image quality,” Image Vision Comput. 10, 451-460 (1992). [CrossRef]
  5. A. VanderLugt, “Signal detection by complex spatial filtering,” IEEE Trans. Inf. Theory 10, 139-145 (1964). [CrossRef]
  6. A. Mahanalobis, B. V. K. Kumar, S. R. F. Sims, and J. Epperson, “Unconstrained correlation filters,” Appl. Opt. 33, 3751-3759 (1994). [CrossRef]
  7. B. Javidi, P. Refregier, and P. Willett, “Optimum receiver design for pattern recognition with nonoverlapping target and scene noise,” Opt. Lett. 18, 1660-1662 (1993). [CrossRef] [PubMed]
  8. A. Yuille, D. Cohen, and P. Hallinan, “Feature extraction from faces using deformable templates,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 1989), pp. 104-109.
  9. S. Lawrence, C. L. Giles, A. C. Tsoi, and A. D. Back, “Face recognition: a convolutional neural network approach,” IEEE Trans. Neural Netw. 8, 98-113 (1997). [CrossRef] [PubMed]
  10. M. A. Neifeld and P. Shankar, “Feature-specific imaging,” Appl. Opt. 42, 3379-3389 (2003). [CrossRef] [PubMed]
  11. P. K. Baheti and M. A. Neifeld, “Feature-specific structured imaging,” Appl. Opt. 45, 7382-7391 (2006). [CrossRef] [PubMed]
  12. M. F. Duarte, M. A. Davenport, M. B. Wakin, and R. G. Baraniuk, “Sparse signal detection from incoherent projections,” in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP, 2006), Vol. 3, pp. 14-19.
  13. D. Takhar, J. N. Laska, M. B. Wakin, M. F. Duarte, D. Baron, S. Sarvotham, K. Kelly, and R. G. Baraniuk, “A new compressive imaging camera architecture using optical-domain compression,” Proc. SPIE 6065, 43-52 (2006).
  14. N. P. Pitsianis, D. J. Brady, and X. Sun, “The quantized cosine transform for sensor-layer image compression,” in Adaptive Optics: Analysis and Methods/Computational Optical Sensing and Imaging/Information Photonics/Signal Recovery and Synthesis Topical Meetings, 2005 OSA Technical Digest Series (Optical Society of America, 2005), paper JMA4.
  15. M. A. Neifeld and J. Ke, “Optical architectures for compressive imaging,” Appl. Opt. 46, 5293-5303 (2007). [CrossRef] [PubMed]
  16. H. S. Pal, D. Ganotra, and M. A. Neifeld, “Face recognition by using feature-specific imaging,” Appl. Opt. 44, 3784-3794(2005). [CrossRef] [PubMed]
  17. P. K. Baheti and M. A. Neifeld, “Adaptive feature-specific imaging: a face recognition example,” Appl. Opt. 47, B21-B31(2008). [CrossRef] [PubMed]
  18. A. Wald, “Sequential Analysis of statistical hypotheses,” Ann. Math. Stat. 16, 117-176 (1945). [CrossRef]
  19. P. Armitage, “Sequential analysis with more than two alternative hypotheses and its relation to discriminant function analysis,” J. R. Stat. Soc. Ser. B. Methodol. 12, 137-144 (1950).
  20. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed. (Wiley-Interscience, 2000).
  21. C. C. Holmes and N. M. Adams, “A probabilistic nearest neighbour method for statistical pattern recognition,” J. R. Stat. Soc. Ser. B. Methodol. 64, 1-12 (2002). [CrossRef]
  22. E. Marszalec, B. Martinkauppi, M. Soriano, and M. Pietikäinen, “A physics-based face database for color research,” J. Electron. Imaging 9, 32-38 (2000). [CrossRef]
  23. N. A. Goodman, P. R. Venkata, and M. A. Neifeld, “Adaptive waveform design and sequential hypothesis testing for target recognition using cognitive radar,” IEEE J. Sel. Top. Signal Process. 1, 105-113 (2007). [CrossRef]
  24. H. H. Barrett and K. J. Myers, Foundations of Image Science, Pure and Applied Optics (Wiley, 2004).
  25. S. Kay, Statistical Signal Processing--Detection Theory (Prentice-Hall PTR, 1998).
  26. P. K. Baheti and M. A. Neifeld, “Random projections based feature-specific structured imaging,” Opt. Express 16, 1764-1776 (2008). [CrossRef] [PubMed]

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