Use of GMM and SCMS for Accurate Road Centerline Extraction from the Classified Image

Zelang Miao, Bin Wang, Wen Zhong Shi, Hao Wu, Yiliang Wan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)

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.
Original languageEnglish
Article number784504
JournalJournal of Sensors
Volume2015
DOIs
Publication statusPublished - 1 Jan 2015

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Instrumentation
  • Electrical and Electronic Engineering

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