Dr. Joseph P. Reynolds Profile

Dr. Joseph P. Reynolds

Sensor Performance Branch Chief at US Army RDECOM CERDEC NVESD

SPIE Involvement:

Conference Program Committee | Author

Publications (41)

PROCEEDINGS ARTICLE | May 23, 2018

A target detection multi-layer matched filter for color and hyperspectral cameras

Proc. SPIE. 10625, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXIX

KEYWORDS: Target detection, Signal to noise ratio, Optical filters, 3D acquisition, Cameras, Color difference, Image filtering, Electronic filtering, Signal detection, RGB color model

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PROCEEDINGS ARTICLE | May 9, 2018

Performance of simulated asynchronous detectors

Proc. SPIE. 10625, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXIX

KEYWORDS: Edge detection, Detection and tracking algorithms, Imaging systems, Cameras, Sensors, Retina, Silicon, Scene simulation, Device simulation

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PROCEEDINGS ARTICLE | May 5, 2017

Optimization of display viewing distance for human observers in the noise-limited case

Proc. SPIE. 10197, Degraded Environments: Sensing, Processing, and Display 2017

KEYWORDS: Target detection, Signal to noise ratio, Eye, Visual process modeling, Data modeling, Visualization, Sensors, Computer simulations, Night vision, Integrated modeling, Visual system, Modulation transfer functions, Target acquisition, Performance modeling, Targeting Task Performance metric

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PROCEEDINGS ARTICLE | May 3, 2016

Reflective band image generation in the night vision integrated performance model

Proc. SPIE. 9820, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXVII

KEYWORDS: Imaging systems, Cameras, Sensors, Image processing, Interference (communication), Modulation transfer functions, Performance modeling, Systems modeling, Device simulation, Hyperspectral simulation

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PROCEEDINGS ARTICLE | May 20, 2015

Thermal image enhancement construct for seeing through obscurants

Proc. SPIE. 9496, Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII

KEYWORDS: Long wavelength infrared, Thermography, Infrared imaging, Mid-IR, Independent component analysis, Data modeling, Cameras, Sensors, Image enhancement, Neodymium

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PROCEEDINGS ARTICLE | May 12, 2015

An independent survey of monochrome and color low light level TV cameras

Proc. SPIE. 9452, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXVI

KEYWORDS: Signal to noise ratio, Optical filters, Visual process modeling, Imaging systems, Cameras, Sensors, Quantum efficiency, Performance modeling, Systems modeling, Targeting Task Performance metric

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PROCEEDINGS ARTICLE | May 12, 2015

Face acquisition camera design using the NV-IPM image generation tool

Proc. SPIE. 9452, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXVI

KEYWORDS: Signal to noise ratio, Facial recognition systems, Detection and tracking algorithms, Imaging systems, Cameras, Image processing, Computer simulations, Image quality, Systems modeling, Device simulation

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PROCEEDINGS ARTICLE | May 12, 2015

A TRM4 component for the Night Vision Integrated Performance Model (NV-IPM)

Proc. SPIE. 9452, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXVI

KEYWORDS: Signal to noise ratio, Eye, Visual process modeling, Modulation, Imaging systems, Cameras, Phase shift keying, Modulation transfer functions, Performance modeling, Systems modeling

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SPIE Journal Paper | April 28, 2014

Human vision noise model validation for the U.S. Army sensor performance metric

Preece, Bradley , Olson, Jeffrey , Reynolds, Joseph , Fanning, Jonathan , Haefner, David

OE Vol. 53 Issue 06

KEYWORDS: Contrast transfer function, Eye, Eye models, Modulation transfer functions, Visual process modeling, Targeting Task Performance metric, Data modeling, Performance modeling, Imaging systems, Calibration

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SPIE Journal Paper | June 5, 2012

Infrared detector size: how low should you go?

Driggers, Ronald , Vollmerhausen, Richard , Reynolds, Joseph , Fanning, Jonathan , Holst, Gerald

OE Vol. 51 Issue 6

KEYWORDS: Sensors, Infrared detectors, Long wavelength infrared, Infrared sensors, Mid-IR, Infrared radiation, Staring arrays, Photons, Signal to noise ratio, Imaging systems

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PROCEEDINGS ARTICLE | May 24, 2012

Human target acquisition performance

Proc. SPIE. 8355, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXIII

KEYWORDS: Target detection, Short wave infrared radiation, Weapons, Imaging systems, Target recognition, Target acquisition, Contrast transfer function, Performance modeling, Systems modeling, Targeting Task Performance metric

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PROCEEDINGS ARTICLE | May 18, 2012

Validating an analytical technique for calculating detection probability given time-dependent search parameters

Proc. SPIE. 8355, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXIII

KEYWORDS: Target detection, Roads, Visual process modeling, Sensors, Video, Night vision, Solids, Target acquisition, Probability theory, Performance modeling

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PROCEEDINGS ARTICLE | May 18, 2012

A standard data set for performance analysis of advanced IR image processing techniques

Proc. SPIE. 8355, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXIII

KEYWORDS: Thermography, Image fusion, Mid-IR, Super resolution, Image compression, Detection and tracking algorithms, Imaging systems, Cameras, Image processing, Black bodies

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PROCEEDINGS ARTICLE | May 18, 2012

Infrared detector size: how low should you go?

Proc. SPIE. 8355, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXIII

KEYWORDS: Staring arrays, Infrared detectors, Long wavelength infrared, Signal to noise ratio, Infrared sensors, Mid-IR, Sensors, Photons, Detector arrays, Infrared radiation

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PROCEEDINGS ARTICLE | June 8, 2011

The physiology of keystroke dynamics

Proc. SPIE. 8058, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX

KEYWORDS: Eye, Biometrics, Sensors, Retina, Physiology, Nervous system, Neurons, Cones, Behavioral biometrics, Brain

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A universal implementation for most behavioral Biometric systems is still unknown since some behaviors aren't individual enough for identification. Habitual behaviors which are measurable by sensors are considered 'soft' biometrics (i.e., walking style, typing rhythm), while physical attributes (i.e., iris, fingerprint) are 'hard' biometrics. Thus, biometrics can aid in the identification of a human not only in cyberspace but in the world we live in. Hard biometrics have proven to be a rather successful form of identification, despite a large amount of individual signatures to keep track of. Virtually all soft biometric strategies, however, share a common pitfall. Instead of the classical pass/fail decision based on the measurements used by hard biometrics, a confidence threshold is imposed, increasing False Alarm and False Rejection Rates. This unreliability is a major roadblock for large scale system integration. Common computer security requires users to log-in with a six or more digit PIN (Personal Identification Number) to access files on the disk. Commercially available Keystroke Dynamics (KD) software can separately calculate and keep track of the mean and variance for each time travelled between each key (air time), and the time spent pressing each key (touch time). Despite its apparent utility, KD is not yet a robust, fault-tolerant system. We begin with a simple question: how could a pianist quickly control so many different finger and wrist movements to play music? What information, if any, can be gained from analyzing typing behavior over time? Biology has shown us that the separation of arm and finger motion is due to 3 long nerves in each arm; regulating movement in different parts of the hand. In this paper we wish to capture the underlying behavioral information of a typist through statistical memory and non-linear dynamics. Our method may reveal an inverse Compressive Sensing mapping; a unique individual signature.

PROCEEDINGS ARTICLE | May 10, 2011

TOD to TTP calibration