Improved K-means Clustering Color Segmentation for Road Perception

DOI

10.2991/ameii-15.2015.198How to use a DOI?

Keywords

Road Perception; Color Model; K-means Clustering Segmentation; B-splines Curve Model.

Abstract

A modified road perception algorithm is presented based on the color image clustering segmentation. According to the comparison of color spaces' uniformity and integrity, an improved K-means clustering algorithm is proposed to segment color images in the space LAB. Firstly, the target area contains road which is gained in images class using the connected domain labeling algorithm. Then, credible road edge points can be obtained in response to alternate-line sampling labeled region of images and assuming the constant of road width consequently. By establishing the B-splines curve model to fit road shape, the algorithm adopts the least square method used to search the optimal control points of splines curve to identify the road boundaries.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Lei Zhou
AU  - Yanjun Zhang
AU  - Danwen Peng
AU  - Dimin Wu
PY  - 2015/04
DA  - 2015/04
TI  - Improved K-means Clustering Color Segmentation for Road Perception
BT  - Proceedings of the International Conference on Advances in Mechanical Engineering and Industrial Informatics
PB  - Atlantis Press
SP  - 1074
EP  - 1079
SN  - 2352-5401
UR  - https://doi.org/10.2991/ameii-15.2015.198
DO  - 10.2991/ameii-15.2015.198
ID  - Zhou2015/04
ER  -