Retinex Image Processing--Publications--SPIE 6978, Orlando, Florida (2008)
Conference
Scene context dependency of pattern constancy of time series imagery
Glenn A. Woodell, Daniel J. Jobson, Zia-ur Rahman
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.
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