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.
In the last few years, consumer Graphics Processor Units (GPUs) have been evolving from fixed-function display generators into general purpose parallel computers. We have explored the potential uses and limitations of this emerging technology as a video coprocessor for real-time image processing applications such as video enhancement, tracking, video stabilisation and multi-sensor fusion. We show how a GPU can be used to implement and accelerate some of these common tasks and show our results. We also address the problem of integrating a GPU into a rugged system in order to deploy this capability into the environments encountered in many defence and security applications.
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