Mr. Douglas C. McKee Profile

Douglas C. McKee

Systems Engineer at L3Harris Technologies Inc

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Publications (7)

Proceedings Article | 12 September 2003 Paper

Real-time IR/EO scene simulator (RISS) product improvements

Douglas McKee, Onda Simmons, Robert Makar, Mark Nuwer, Paul Palumbo, Paul Schlossman, Roderic Perry, Louis Chan

Proceedings Volume 5092, (2003) https://doi.org/10.1117/12.501200

KEYWORDS: Transparency, Computer simulations, Sensors, Scene simulation, Data modeling, Human-machine interfaces, Image processing, Visualization, Clouds, Image analysis

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Proceedings Article | 31 August 2001 Paper

Advances in the universal programmable interface (UPI) for hardware-in-the-loop (HWIL) sensor stimulation

Matthew Weppner, Douglas McKee, Paul Schlossman, Karl Robinson, Mark Nuwer, Leonard Smith, Daniel Howe, Heather Lagowski

Proceedings Volume 4366, (2001) https://doi.org/10.1117/12.438084

KEYWORDS: Sensors, Infrared sensors, Interfaces, Image sensors, Projection systems, Distortion, Video, Computer simulations, Infrared radiation, Infrared imaging

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Proceedings Article | 12 July 2000 Paper

Advances in the universal programmable interface

Douglas McKee

Proceedings Volume 4027, (2000) https://doi.org/10.1117/12.391683

KEYWORDS: Sensors, Convolution, Interfaces, 3D modeling, Modulation transfer functions, Interference (communication), Projection systems, Algorithm development, Video, Image sensors

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Proceedings Article | 19 July 1999 Paper

Sensor modeling using a universal programmable interface for hardware in-the-loop IR/EO testing

Douglas McKee, Daniel Howe, Charles Ferrara

Proceedings Volume 3697, (1999) https://doi.org/10.1117/12.352929

KEYWORDS: Sensors, Modulation transfer functions, Convolution, Performance modeling, Electro optical modeling, Systems modeling, Infrared sensors, Interfaces, Image sensors, Software development

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Proceedings Article | 17 July 1998 Paper

Multistage foveal target detection system

Douglas McKee, Cesar Bandera

Proceedings Volume 3374, (1998) https://doi.org/10.1117/12.327097

KEYWORDS: Target detection, Neural networks, Image filtering, Principal component analysis, Image resolution, Forward looking infrared, Detection and tracking algorithms, Sensors, Neurons, Machine vision

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Proceedings Article | 28 July 1997 Paper

Performance of a foveal target-detection system

Douglas McKee, Cesar Bandera

Proceedings Volume 3068, (1997) https://doi.org/10.1117/12.280844

KEYWORDS: Neural networks, Target detection, Principal component analysis, Sensors, Machine vision, Active vision, Astatine, Neurons, Optical sensors, Image processing

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Proceedings Article | 14 June 1996 Paper

Model-based automatic target recognition using hierarchical foveal machine vision

Douglas McKee, Cesar Bandera, Sugata Ghosal, Patrick Rauss

Proceedings Volume 2755, (1996) https://doi.org/10.1117/12.243150

KEYWORDS: Target detection, Fractal analysis, Automatic target recognition, Sensors, Image filtering, Visual process modeling, Machine vision, Image resolution, Model-based design, Target recognition

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This paper presents a target detection and interrogation techniques for a foveal automatic target recognition (ATR) system based on the hierarchical scale-space processing of imagery from a rectilinear tessellated multiacuity retinotopology. Conventional machine vision captures imagery and applies early vision techniques with uniform resolution throughout the field-of-view (FOV). In contrast, foveal active vision features graded acuity imagers and processing coupled with context sensitive gaze control, analogous to that prevalent throughout vertebrate vision. Foveal vision can operate more efficiently in dynamic scenarios with localized relevance than uniform acuity vision because resolution is treated as a dynamically allocable resource. Foveal ATR exploits the difference between detection and recognition resolution requirements and sacrifices peripheral acuity to achieve a wider FOV (e.g. faster search), greater localized resolution where needed (e.g., more confident recognition at the fovea), and faster frame rates (e.g., more reliable tracking and navigation) without increasing processing requirements. The rectilinearity of the retinotopology supports a data structure that is a subset of the image pyramid. This structure lends itself to multiresolution and conventional 2-D algorithms, and features a shift invariance of perceived target shape that tolerates sensor pointing errors and supports multiresolution model-based techniques. The detection technique described in this paper searches for regions-of- interest (ROIs) using the foveal sensor's wide FOV peripheral vision. ROIs are initially detected using anisotropic diffusion filtering and expansion template matching to a multiscale Zernike polynomial-based target model. Each ROI is then interrogated to filter out false target ROIs by sequentially pointing a higher acuity region of the sensor at each ROI centroid and conducting a fractal dimension test that distinguishes targets from structured clutter.

Showing 5 of 7 publications

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