Use of GMM and SCMS for Accurate Road Centerline Extraction from the Classified Image
Joint Authors
Shi, Wenzhong
Wu, Hao
Wan, Yiliang
Miao, Zelang
Wang, Bin
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-10-21
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Miao, Zelang& Wang, Bin& Shi, Wenzhong& Wu, Hao& Wan, Yiliang. 2015. Use of GMM and SCMS for Accurate Road Centerline Extraction from the Classified Image. Journal of Sensors،Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1070196
Modern Language Association (MLA)
Miao, Zelang…[et al.]. Use of GMM and SCMS for Accurate Road Centerline Extraction from the Classified Image. Journal of Sensors No. 2015 (2015), pp.1-13.
https://search.emarefa.net/detail/BIM-1070196
American Medical Association (AMA)
Miao, Zelang& Wang, Bin& Shi, Wenzhong& Wu, Hao& Wan, Yiliang. Use of GMM and SCMS for Accurate Road Centerline Extraction from the Classified Image. Journal of Sensors. 2015. Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1070196
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1070196