Minimum reconstruction error in feature-specific imaging

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

doi:10.1117/12.603059

Minimum reconstruction error in feature-specific imaging

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

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

7 Citations (Scopus)

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 Qnd 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.

Original language	English
Article number	02
Pages (from-to)	7-12
Number of pages	6
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	5817
DOIs	https://doi.org/10.1117/12.603059
Publication status	Published - 2005
Externally published	Yes
Event	Visual Information Processing XIV - Orlando, FL, United States Duration: 29 Mar 2005 → 30 Mar 2005

Keywords

Feature-Specific Imaging
Image reconstruction
PCA
Stochastic Tunneling
Weiner operator

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1117/12.603059

Cite this

@article{2f9f0f28e21848b6be3decbfa02162f2,

title = "Minimum reconstruction error in feature-specific imaging",

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 Qnd 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.",

keywords = "Feature-Specific Imaging, Image reconstruction, PCA, Stochastic Tunneling, Weiner operator",

author = "Jun Ke and Stenner, {Michael D.} and Neifeld, {Mark A.}",

year = "2005",

doi = "10.1117/12.603059",

language = "English",

volume = "5817",

pages = "7--12",

journal = "Proceedings of SPIE - The International Society for Optical Engineering",

issn = "0277-786X",

publisher = "SPIE",

note = "Visual Information Processing XIV ; Conference date: 29-03-2005 Through 30-03-2005",

}

TY - JOUR

T1 - Minimum reconstruction error in feature-specific imaging

AU - Ke, Jun

AU - Stenner, Michael D.

AU - Neifeld, Mark A.

PY - 2005

Y1 - 2005

N2 - 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 Qnd 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.

AB - 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 Qnd 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.

KW - Feature-Specific Imaging

KW - Image reconstruction

KW - PCA

KW - Stochastic Tunneling

KW - Weiner operator

UR - http://www.scopus.com/inward/record.url?scp=27644566559&partnerID=8YFLogxK

U2 - 10.1117/12.603059

DO - 10.1117/12.603059

M3 - Conference article

AN - SCOPUS:27644566559

SN - 0277-786X

VL - 5817

SP - 7

EP - 12

JO - Proceedings of SPIE - The International Society for Optical Engineering

JF - Proceedings of SPIE - The International Society for Optical Engineering

M1 - 02

T2 - Visual Information Processing XIV

Y2 - 29 March 2005 through 30 March 2005

ER -

Minimum reconstruction error in feature-specific imaging

Abstract

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this