TY  - CONF
AU  - Jose Manuel Alvarez
AU  - Theo Gevers
AU  - Antonio Lopez
A2  - CVPR
PY  - 2009//
TI  - Learning Photometric Invariance from Diversified Color Model Ensembles
BT  - 22nd IEEE Conference on Computer Vision and Pattern Recognition
SP  - 565–572
KW  - road detection
N2  - Color is a powerful visual cue for many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions affecting negatively the performance of the task at hand. Often, a reflection model (e.g., Lambertian or dichromatic reflectance) is used to derive color invariant models. However, those reflection models might be too restricted to model real-world scenes in which different reflectance mechanisms may hold simultaneously. Therefore, in this paper, we aim to derive color invariance by learning from color models to obtain diversified color invariant ensembles. First, a photometrical orthogonal and non-redundant color model set is taken on input composed of both color variants and invariants. Then, the proposed method combines and weights these color models to arrive at a diversified color ensemble yielding a proper balance between invariance (repeatability) and discriminative power (distinctiveness). To achieve this, the fusion method uses a multi-view approach to minimize the estimation error. In this way, the method is robust to data uncertainty and produces properly diversified color invariant ensembles. Experiments are conducted on three different image datasets to validate the method. From the theoretical and experimental results, it is concluded that the method is robust against severe variations in imaging conditions. The method is not restricted to a certain reflection model or parameter tuning. Further, the method outperforms state-of- the-art detection techniques in the field of object, skin and road recognition.
SN  - 1063-6919
SN  - 978-1-4244-3992-8
L1  - http://refbase.cvc.uab.es/files/AGL2009a.pdf
UR  - http://dx.doi.org/10.1109/CVPR.2009.5206785
N1  - ADAS;ISE
ID  - Jose Manuel Alvarez2009
ER  -