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Title: Use of GMM and SCMS for accurate road centerline extraction from the classified image
Authors: Miao, Z 
Wang, B 
Shi, W 
Wu, H
Wan, Y
Issue Date: 2015
Publisher: Hindawi Publishing Corporation
Source: Journal of sensors, 2015, v. 2015, 784504 How to cite?
Journal: Journal of sensors 
Abstract: The extraction of road centerline from the classified image is a fundamental image analysis technology. Common problems encountered in road centerline extraction include low ability for coping with the general case, production of undesired objects, and inefficiency. To tackle these limitations, this paper presents a novel accurate centerline extraction method using Gaussian mixture model (GMM) and subspace constraint mean shift (SCMS). The proposed method consists of three main steps. GMM is first used to partition the classified image into several clusters. The major axis of the ellipsoid of each cluster is extracted and deemed to be taken as the initial centerline. Finally, the initial result is adjusted using SCMS to produce precise road centerline. Both simulated and real datasets are used to validate the proposed method. Preliminary results demonstrate that the proposed method provides a comparatively robust solution for accurate centerline extraction from a classified image.
ISSN: 1687-725X
EISSN: 1687-7268
DOI: 10.1155/2015/784504
Rights: Copyright © 2015 Zelang Miao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The following article: Miao, Z., Wang, B., Shi, W., Wu, H., & Wan, Y. (2015). Use of GMM and SCMS for accurate road centerline extraction from the classified image. Journal of Sensors, 2015, is available at https//
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