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Proceedings Paper

Scene context dependency of pattern constancy of time series imagery
Author(s): Glenn Woodell; Daniel J. Jobson; Zia-ur Rahman
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Paper Abstract

A fundamental element of future generic pattern recognition technology is the ability to extract similar patterns for the same scene despite wide ranging extraneous variables, including lighting, turbidity, sensor exposure variations, and signal noise. In the process of demonstrating pattern constancy of this kind for retinex/visual servo (RVS) image enhancement processing, we found that the pattern constancy performance depended somewhat on scene content. Most notably, the scene topography and, in particular, the scale and extent of the topography in an image, affects the pattern constancy the most. This paper will explore these effects in more depth and present experimental data from several time series tests. These results further quantify the impact of topography on pattern constancy. Despite this residual inconstancy, the results of overall pattern constancy testing support the idea that RVS image processing can be a universal front-end for generic visual pattern recognition. While the effects on pattern constancy were significant, the RVS processing still does achieve a high degree of pattern constancy over a wide spectrum of scene content diversity, and wide ranging extraneousness variations in lighting, turbidity, and sensor exposure.

Paper Details

Date Published: 25 March 2008
PDF: 12 pages
Proc. SPIE 6978, Visual Information Processing XVII, 69780L (25 March 2008); doi: 10.1117/12.778676
Show Author Affiliations
Glenn Woodell, NASA Langley Research Ctr. (United States)
Daniel J. Jobson, NASA Langley Research Ctr. (United States)
Zia-ur Rahman, Old Dominion Univ. (United States)

Published in SPIE Proceedings Vol. 6978:
Visual Information Processing XVII
Zia-ur Rahman; Stephen E. Reichenbach; Mark Allen Neifeld, Editor(s)

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