Mr. Jim Morgenstern Profile

Jim Morgenstern

Vice President, Marketing at Vision4ce LLC

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Proceedings Article | 18 May 2013 Paper

Improved target recognition with live atmospheric correction

Cynthia Archer, James Morgenstern

Proceedings Volume 8743, 87430O (2013) https://doi.org/10.1117/12.2015741

KEYWORDS: Calibration, Cameras, Reflectivity, Atmospheric modeling, Sensors, Solar energy, Target detection, Atmospheric corrections, Atmospheric sensing, Imaging systems

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Hyperspectral airborne sensing systems frequently employ spectral signature databases to detect materials. To achieve high detection and low false alarm rates, it is critical to retrieve accurate reflectance values from the camera’s digital number (dn) output. A one-time camera calibration converts dn values to reflectance. However, changes in solar angle and atmospheric conditions distort the reflected energy, reducing detection performance of the system.

Changes in solar angle and atmospheric conditions introduce both additive (offset) and multiplicative (gain) effects for each waveband. A gain and offset correction can mitigate these effects. Correction methods based on radiative transfer models require equipment to measure solar angle and atmospheric conditions. Other methods use known reference materials in the scene to calculate the correction, but require an operator to identify the location of these materials. Our unmanned airborne vehicles application can use no additional equipment or require operator intervention. Applicable automated correction approaches typically analyze gross scene statistics to find the gain and offset values. Airborne hyperspectral systems have high ground resolution but limited fields-of-view, so an individual frame does not include all the variation necessary to accurately calculate global statistics.

In the present work we present our novel approach to the automatic estimation of atmospheric and solar effects from the hyperspectral data. Our approach is based on Hough transform matching of background spectral signatures with materials extracted from the scene. Scene materials are identified with low complexity agglomerative clustering. Detection results with data gathered from recent field tests are shown.

Proceedings Article | 20 May 2011 Paper

GPGPU-based real-time conditional dilation for adaptive thresholding for target detection

Jim Morgenstern, Bradley Zell

Proceedings Volume 8048, 80480P (2011) https://doi.org/10.1117/12.890851

KEYWORDS: Image processing, Target detection, Optical filters, Detection and tracking algorithms, Hyperspectral target detection, Sensors, Image analysis, Data processing, Image compression, Parallel computing

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A significant topic in many image processing systems is the derivation of a threshold to actuate the automated analysis of outputs from spectral filters and/or anomaly filters, the detection of targets and/or classes of objects which are different than the local background clutter. There are cases where the signals of interest have contrast locally against their immediate surroundings but the application of a global threshold over the entire image produces poor results with missed detections and numerous false alarms. In such cases an adaptive or local threshold operator offers a more robust solution. One local threshold function is the conditional dilation which produces a reference image via a series of dilations which are conditioned on not exceeding the signal levels in the original image. In the limit this reference image becomes a threshold surface where only areas or objects exhibiting contrast locally remain after application of the threshold. Algorithms have been introduced which enable use of conditional dilation in realtime systems by reducing the unbounded series of dilations to a small, fixed number of operations. In the present work we present an adaptation of this algorithm to both single CPU systems and also to systems which incorporate a GPGPU device which enables a highly parallel version of the algorithm subject to the unique architecture constraints of the GPGPU. Execution timings for comparison are introduced: The GPGPU offers somewhat better performance than the single CPU system despite the GPGPU architecture not being suitable for implementation of a neighborhood process.

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