General linear hypothesis test: a method for algorithm selection

Paul Singerman; Erik Blasch; Michael Giansiracusa; Soundararajan Ezekiel

doi:10.1117/12.2262929

6 June 2017 General linear hypothesis test: a method for algorithm selection

Paul Singerman, Erik Blasch, Michael Giansiracusa, Soundararajan Ezekiel

Author Affiliations +

Proceedings Volume 10199, Geospatial Informatics, Fusion, and Motion Video Analytics VII; 101990E (2017) https://doi.org/10.1117/12.2262929
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

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.

Conference Presentation

Citation Download Citation

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 (6 June 2017); https://doi.org/10.1117/12.2262929

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