Please submit manuscripts in either of the following two submission systems

    ScholarOne Manuscripts

  • ScholarOne

    • 勤云稿件系统

  • 登录

Search by Issue

  • 2024 Vol.31
  • 2023 Vol.30
  • 2022 Vol.29
  • 2021 Vol.28
  • 2020 Vol.27
  • 2019 Vol.26
  • 2018 Vol.25
  • 2017 Vol.24
  • 2016 vol.23
  • 2015 vol.22
  • 2014 vol.21
  • 2013 vol.20
  • 2012 vol.19
  • 2011 vol.18
  • 2010 vol.17
  • 2009 vol.16
  • No.1
  • No.2

Search by Keywords

  • Search by Title
  • Search by Keywords
  • Search by Abstract
  • Search by Author's Chinese Name
  • Search by Author's English Name
  • Search by Fund

News & AnnouncementMORE

Supervised by Ministry of Industry and Information Technology of The People's Republic of China Sponsored by Harbin Institute of Technology Editor-in-chief Yu Zhou ISSNISSN 1005-9113 CNCN 23-1378/T

期刊网站二维码
微信公众号二维码
Related citation:FENG Xiao-qiang,HE Tie-jun.Fast matching pursuit for traffic images using differential evolution[J].Journal of Harbin Institute Of Technology(New Series),2010,17(2):193-198.DOI:10.11916/j.issn.1005-9113.2010.02.009.
【Print】   【HTML】   【PDF download】   【View/Add Comment】  【Download reader】  【 Close 】
←Previous|Next→ Back Issue    Advanced Search
This paper has been: browsed 1024times   downloaded 610times 本文二维码信息
码上扫一扫！
Shared by: Wechat More
Font:larger+|default|smaller-
Fast matching pursuit for traffic images using differential evolution
Author NameAffiliation
FENG Xiao-qiang Intelligent Transportation System(ITS) Research Center,South East University,Nanjing 210096,China 
HE Tie-jun Intelligent Transportation System(ITS) Research Center,South East University,Nanjing 210096,China 
Abstract:
To obtain the sparse decomposition and flexible representation of traffic images,this paper proposes a fast matching pursuit for traffic images using differential evolution. According to the structural features of traffic images,the introduced algorithm selects the image atoms in a fast and flexible way from an over-complete image dictionary to adaptively match the local structures of traffic images and therefore to implement the sparse decomposition. As compared with the traditional method and a genetic algorithm of matching pursuit by using extensive experiments,the differential evolution achieves much higher quality of traffic images with much less computational time,which indicates the effectiveness of the proposed algorithm.
Key words:  intelligent transportation system  digital image processing  matching pursuit  differential evolution
DOI：10.11916/j.issn.1005-9113.2010.02.009
Clc Number:TP391.41
Fund:

LINKS

Copyright © The Editorial Department of Journal of Harbin Institute of Technology
Address: 92 West Dazhi Street, Nan Gang District, Harbin, Heilongjiang Province, China Postcode: 150001
(C)2015 JOURNAL OF HIT ALL RIGHTS RESERVED. ICP. 0000423255