Title: Non-parametric Classification of Pixels Under 
Varying Outdoor Illumination

Authors: Shashi D. Buluswar and Bruce A. Draper

Affiliation: Computer Vision Research Laboratory, Dept. of Computer Science,
	University of Massachusetts, Box 34610, Amherst, MA 01003-4610

Abstract:  

Using color for visual recognition outdoors has proven to be a difficult
problem, chiefly due to varying illumination.  Attempts to classify pixels 
or image patches in outdoor scenes often fail, partly because of the paucity
of data, but partly because color shifts due to changes in illumination 
are not well modeled as random noise.  Approaches which attempt to recover
the "true color" of the incident light (i.e. color constancy approaches) 
appear to work in constrained environments, but are not yet applicable to
outdoor scenes.
	
We present a technique that uses training images of an object under daylight
to learn the shift in color of an object.  Our method uses multivariate 
decision trees for piecewise linear approximation of the region corresponding
to the object's appearance in color space.  We then classify pixels in outdoor
scenes depending on whether they fall within this region, and group clusters of
target pixels into regions of interest (ROIs) for a model-based RSTA system.
The techniques presented are demonstrated on a challenging task: recognizing
camouflaged vehicles in outdoor military scenes.

Keywords:
Color, Pixel classification, Decision Trees, Chromatic Variation,
Focus of Attention