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Extracting Scale and Illuminant Invariant Regions through Color
journal contribution
posted on 2006-01-01, 00:00 authored by Ranjith Unnikrishnan, Martial HebertDespite the fact that color is a powerful cue in object recognition, the extraction
of scale-invariant interest regions from color images frequently begins
with a conversion of the image to grayscale. The isolation of interest points
is then completely determined by luminance, and the use of color is deferred
to the stage of descriptor formation. This seemingly innocuous conversion
to grayscale is known to suppress saliency and can lead to representative
regions being undetected by procedures based only on luminance. Furthermore,
grayscaled images of the same scene under even slightly different illuminants
can appear sufficiently different as to affect the repeatability of
detections across images. We propose a method that combines information
from the color channels to drive the detection of scale-invariant keypoints.
By factoring out the local effect of the illuminant using an expressive linear
model, we demonstrate robustness to a change in the illuminant without
having to estimate its properties from the image. Results are shown on challenging
images from two commonly used color constancy datasets.