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Proceedings Article

Minimum reconstruction error in feature-specific imaging

[+] Author Affiliations
Jun Ke, Michael D. Stenner

Univ. of Arizona (USA)

Mark A. Neifeld

Optical Sciences Ctr./Univ. of Arizona (USA)

Proc. SPIE 5817, Visual Information Processing XIV, 7 (May 31, 2005); doi:10.1117/12.603059
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From Conference Volume 5817

  • Visual Information Processing XIV
  • Zia-ur Rahman; Robert A. Schowengerdt; Stephen E. Reichenbach
  • Orlando, Florida, USA | March 28, 2005

abstract

We describe theoretical and experimental results for a new class of optimal features for feature-specific imaging (FSI). In this paper, we theoretically solve the reconstruction problem without noise, and find a more general solution than principle component analysis (PCA). We present a generalized framework to find FSI projection matrices. Using Stochastic Tunneling, we find an optimal solution in the presence of noise and under an energy conservation constraint. We also show that a non-negativity requirement does not significantly reduce system performance. Finally, we propose an experimental system for FSI using a polarization-based optical pipeline processor.

© (2005) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

Jun Ke ; Michael D. Stenner and Mark A. Neifeld
"Minimum reconstruction error in feature-specific imaging", Proc. SPIE 5817, Visual Information Processing XIV, 7 (May 31, 2005); doi:10.1117/12.603059; http://dx.doi.org/10.1117/12.603059


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