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Proceedings Paper

Monotonic correlation analysis of image quality measures for image fusion
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Paper Abstract

The next generation of night vision goggles will fuse image intensified and long wave infra-red to create a hybrid image that will enable soldiers to better interpret their surroundings during nighttime missions. Paramount to the development of such goggles is the exploitation of image quality measures to automatically determine the best image fusion algorithm for a particular task. This work will introduce a novel monotonic correlation coefficient to investigate how well possible image quality features correlate to actual human performance, which is measured by a perception study. The paper will demonstrate how monotonic correlation can identify worthy features that could be overlooked by the traditional Pearson correlation.

Paper Details

Date Published: 15 April 2008
PDF: 12 pages
Proc. SPIE 6941, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XIX, 69410A (15 April 2008); doi: 10.1117/12.776870
Show Author Affiliations
Lance M. Kaplan, Army Research Lab. (United States)
Stephen D. Burks, Night Vision Electronic Systems Directorate (United States)
Richard K Moore, Univ. of Memphis (United States)
Quang Nguyen, Night Vision Electronic Systems Directorate (United States)


Published in SPIE Proceedings Vol. 6941:
Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XIX
Gerald C. Holst, Editor(s)

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