Dr. Paul E. Keller
Senior Research Scientist at Pacific Northwest National Lab
SPIE Involvement:
Conference Chair | Author | Instructor
Area of Expertise:
image science , neural networks , pattern recognition , optics , machine learning
Profile Summary

Senior Research Scientist with 20+ years experience in three overlapping areas: optics, imaging science, and data analysis as applied to projects spanning non-proliferation, security, defense, medicine, environmental sensing, telecommunications, and energy distribution for a wide range of government and commercial clients.

Specialties
• Optics: System Modeling, Optical Signal Processing, Physical Optics, Photonics, Holography, Imaging Systems
• Imaging Science: Image Formation, Image Reconstruction, Image Processing, Machine Vision, Multimodality Fusion, Image & Video Analytics (millimeter wave [active and passive], THz, IR, visible, UV, X-ray, Gamma Ray, MRI, Ultrasound)
• Data Analysis: Machine Learning, Neural Networks, Information Physics, Signal Processing, Pattern Recognition, Statistics
Publications (14)

Proceedings Article | 17 October 2012
Proc. SPIE. 8506, Developments in X-Ray Tomography VIII
KEYWORDS: Phase contrast, Visualization, Interferometers, X-rays, Image resolution, Spatial resolution, X-ray imaging, Osmium, Visibility, Absorption

Proceedings Article | 1 May 2009
Proc. SPIE. 7324, Atmospheric Propagation VI
KEYWORDS: Signal to noise ratio, Telescopes, Retroreflectors, Speckle, LIDAR, Numerical analysis, Speckle pattern, Monte Carlo methods, Turbulence, Solids

Proceedings Article | 30 April 2009
Proc. SPIE. 7347, Evolutionary and Bio-Inspired Computation: Theory and Applications III
KEYWORDS: Principal component analysis, Data modeling, Sensors, Diagnostics, Control systems, Xenon, Analytical research, Data centers, Systems modeling, Neurons

Proceedings Article | 30 April 2009
Proc. SPIE. 7309, Passive Millimeter-Wave Imaging Technology XII
KEYWORDS: Holography, 3D acquisition, 3D image reconstruction, Detection and tracking algorithms, Sensors, Image segmentation, Scanners, Dielectrics, Image restoration, 3D image processing

Proceedings Article | 28 February 2006
Proc. SPIE. 6128, Photonic Crystal Materials and Devices IV
KEYWORDS: Chalcogenide glass, Waveguides, Crystals, Dielectrics, Silver, Chalcogenides, Photonic crystals, Infrared radiation, Infrared photography, Infrared materials

SPIE Press Book | 30 August 2005
KEYWORDS: Neural networks, Data modeling, Artificial neural networks, Error analysis, Image segmentation, Pattern recognition, Sensors, Evolutionary algorithms, Statistical analysis

Showing 5 of 14 publications
Conference Committee Involvement (4)
Applications and Science of Computational Intelligence V
2 April 2002 | Orlando, FL, United States
Applications and Science of Computational Intelligence IV
17 April 2001 | Orlando, FL, United States
Applications and Science of Computational Intelligence III
24 April 2000 | Orlando, FL, United States
Applications and Science of Computational Intelligence II
5 April 1999 | Orlando, FL, United States
Course Instructor
SC166: Fundamentals of Artificial Neural Networks
The course starts with the history of research into biological neural networks used for unsolved problems in information processing. The technology of physiologically motivated information processing to solve engineering problems had advanced. Neural networks for finding patterns in data have progressed out of the laboratory and into products. This course provides the background to understand and apply this technology for recognizing patterns in data.
SC360: Advanced Neural Networks
Neural networks have been around for over forty years. This course presents many examples of artificial neural networks and provides the attendee with a thorough understanding of the most popular neural networks such as back propagation trained feed-forward neural networks, self-organizing feature maps, adaptive resonance theory (ART), generalized linear and hybrid neural networks. The attendee is given the theoretical background needed to understand why one network or combination of networks works on a given problem but may not be a good choice for others. The instructor introduces the latest algorithms and their applications to many engineering problems.
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