Improved K-means Clustering Color Segmentation for Road Perception
Authors
Lei Zhou, Yanjun Zhang, Danwen Peng, Dimin Wu
Corresponding Author
Lei Zhou
Available Online April 2015.
- 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/).
Cite this article
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 -