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Success Stories
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Pattern Matching
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Pattern matching is used in a broad array of image processing
and security applications: medical imaging, aerospace
and defense, security, machine vision, and others. This
application has the characteristics that it can be very
computationally intensive, it requires interactivity
during the algorithm development stage, and it can involve
very large data sets.
The application commonly makes use of the Fourier Transform
and correlation to locate the target image within the
original image. In the increasing number of real-life
cases, this type of algorithm needs to operate on very
large, high-resolution images, potentially in real time.
Also, it is critical to optimize parameters of the algorithm,
such as filters and thresholds, to achieve best results.
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Star-P is well-suited for this application, supporting
huge images, rapid processing, and iterative optimization,
while still using the familiar MATLAB® environment. Star-P
enables interactive optimization of evolving pattern matching
algorithms, and enables simple conversion of pre-existing
serial code for parallel computing, deals effectively
with supercomputing-scale data sets, and delivers supercomputing-class
performance.
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 Summary
& Metrics
- Interactive development of image processing algorithms in a familiar MATLAB® environment
- Scale to large and growing data sets, in the gigabyte/terabyte range, for rapid development and real-time processing
- An FFT-based pattern matching application on a 6400x6400 image (1GB of data) ran in 21 seconds that previously could not be run on a desktop PC
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"We view Star-P as a major productivity tool to support
D.O.D. customers. Rather than re-coding algorithms in
C and MPI, we are looking to Star-P to let us continue
working in the high productivity desktop MATLAB® environment,
while scaling to supercomputers."
John Nehrbass,
PhD, Director, Computational Signal & Image Processing,
Ohio Supercomputer Center / Wright-Patterson AFB
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