@article{ref1,
title="Multiband image segmentation and object recognition for understanding road scenes",
journal="IEEE transactions on intelligent transportation systems",
year="2011",
author="Kang, Y. and Yamaguchi, K. and Naito, T. and Ninomiya, Y.",
volume="12",
number="4",
pages="1423-1433",
abstract="This paper presents a novel method for semantic segmentation and object recognition in a road scene using a hierarchical bag-of-textons method. Current driving-assistance systems rely on multiple vehicle-mounted cameras to perceive the road environment. The proposed method relies on integrated color and near-infrared images and uses the hierarchical bag-of-textons method to recognize the spatial configuration of objects and extract contextual information from the background. The histogram of the hierarchical bag-of-textons is concatenated to textons extracted from a multiscale grid window to automatically learn the spatial context for semantic segmentation. Experimental results show that the proposed method has better segmentation accuracy than the conventional bag-of-textons method. By integrating it with other scene interpretation systems, the proposed system can be used to understand road scenes for vehicle environment perception.<p />",
language="",
issn="1524-9050",
doi="10.1109/TITS.2011.2160539",
url="http://dx.doi.org/10.1109/TITS.2011.2160539"
}