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Proceedings Paper

General linear hypothesis test: a method for algorithm selection
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Paper Abstract

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.

Paper Details

Date Published: 6 June 2017
PDF: 17 pages
Proc. SPIE 10199, Geospatial Informatics, Fusion, and Motion Video Analytics VII, 101990E (6 June 2017); doi: 10.1117/12.2262929
Show Author Affiliations
Paul Singerman, Indiana Univ. of Pennsylvania (United States)
Erik Blasch, Air Force Research Lab. (United States)
Michael Giansiracusa, Indiana Univ. of Pennsylvania (United States)
Soundararajan Ezekiel, Indiana Univ. of Pennsylvania (United States)


Published in SPIE Proceedings Vol. 10199:
Geospatial Informatics, Fusion, and Motion Video Analytics VII
Kannappan Palaniappan; Peter J. Doucette; Gunasekaran Seetharaman; Anthony Stefanidis, Editor(s)

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