Minimum reconstruction error in feature-specific imaging

Jun Ke; Michael D. Stenner; Mark A. Neifeld

doi:10.1117/12.603059

25 May 2005 Minimum reconstruction error in feature-specific imaging

Jun Ke, Michael D. Stenner, Mark A. Neifeld

Proceedings Volume 5817, Visual Information Processing XIV; (2005) https://doi.org/10.1117/12.603059
Event: Defense and Security, 2005, Orlando, Florida, United States

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.

Citation Download Citation

Jun Ke, Michael D. Stenner, and Mark A. Neifeld "Minimum reconstruction error in feature-specific imaging", Proc. SPIE 5817, Visual Information Processing XIV, (25 May 2005); https://doi.org/10.1117/12.603059

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