Algorithm selection is paramount in determining how to implement a process. When the results can be computed
directly, an algorithm that reduces computational complexity is selected. When the results less binary there can be difficulty
in choosing the proper implementation. Weighing the effect of different pieces of the algorithm on the final result can be
difficult to find. In this research, we propose using a statistical analysis tool known as General Linear Hypothesis to find
the effect of different pieces of an algorithm implementation on the end result. This will be done with transform based
image fusion techniques. This study will weigh the effect of different transforms, fusion techniques, and evaluation metrics
on the resulting images. We will find the best no-reference metric for image fusion algorithm selection and test this method
on multiple types of image sets. This assessment will provide a valuable tool for algorithm selection to augment current
techniques when results are not binary.
Paul Singerman, Erik Blasch, Michael Giansiracusa, and Soundararajan Ezekiel, "General linear hypothesis test: a method for algorithm selection," Proc. SPIE 10199, Geospatial Informatics, Fusion, and Motion Video Analytics VII, 101990E (Presented at SPIE Defense + Security: April 12, 2017; Published: 6 June 2017); https://doi.org/10.1117/12.2262929.
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