This paper describes the design and implementation of a framework used to develop and assess novel classification
algorithms applied to imagery from diverse sources, such as in-service thermal sensors and experimental burst
illumination lasers. The framework is designed to aid with the development of algorithms where both high classification
performance and fast execution are required. It addresses the issue of how to effectively divide development time and
effort between the potentially conflicting tasks of improving performance and speed. The development of the framework
and its implementation in MATLAB® are described, along with three short case studies showing its application during
recent projects.
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