1997
@inproceedings{GeS1997,
vgclass = {refpap},
vgproject = {cbir,invariance},
author = {T. Gevers and A. W. M. Smeulders},
title = {Object Recognition Based on Photometric Color Invariants},
booktitle = {The 10th Scandinavian Conference on Image Analysis (SCIA'97)},
address = {Lappeenranta, Finland},
pages = {861--868},
month = {June},
year = {1997},
abstract = {Our aim is to analyze and evaluate different color models
to be used for the purpose of 3-D object recognition by color-metric
histogram matching according to the following criteria: invariance to
the geometry of the object and illumination circumstances, high
discriminative power, and noise robustness.
Assuming white illumination and dichromatic reflectance, we propose new
color models $c_1c_2c_3$ and $l_1l_2l_3$ invariant to the viewing
direction, object geometry and shading. Further, it is shown that
$l_1l_2l_3$ is also invariant to highlights. Further a change in
spectral power distribution of the illumination is considered to
propose a new photometric color invariant $m_1m_2m_3$ for matte
objects.
To evaluate photometric color invariant object recognition in practice,
experiments have been carried out on a database consisting of 500
images taken from 3-D multicolored man-made objects.
On the basis of the reported theory and experimental results, it is
shown that high object recognition accuracy is achieved by $l_1l_2l_3$
and hue $H$ followed by $c_1c_2c_3$ and normalized colors \emph{rgb}
under the constraint of white illumination. Saturation $S$ and
$m_1m_2m_3$ provide slightly worse object recognition accuracy under
the same imaging conditions. Finally, it is shown that solely
$m_1m_2m_3$ is invariant to a change in illumination color.},
}