20 May 2011 GPGPU-based real-time conditional dilation for adaptive thresholding for target detection
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Abstract
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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Jim Morgenstern, Bradley Zell, "GPGPU-based real-time conditional dilation for adaptive thresholding for target detection", Proc. SPIE 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, 80480P (20 May 2011); doi: 10.1117/12.890851; https://doi.org/10.1117/12.890851
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