Journal of Robotics, Networking and Artificial Life

Volume 4, Issue 1, June 2017, Pages 1 - 4

An accurate method for the extraction of line structured light stripe

Authors
Jiwu Wang, Yaodong Li, Zhijing Jian, Masanori Sugisaka
Corresponding Author
Jiwu Wang
Available Online 1 June 2017.
DOI
10.2991/jrnal.2017.4.1.1How to use a DOI?
Keywords
Line structured light; region segmentation; Rail profile; Machine vision
Abstract

In order to improve the accuracy in the On-line measurement of rail profile with a line structured light based on machine vision, the accurate extraction of a structured light stripe is a necessary and key step. An accurate extraction method is proposed for the structural light stripe in practical applications. The structured light stripe is separated and extracted accurately based on the geometric characteristics of the structure light stripe in the binary image. And the noise in the captured image is removed with region segmentation method. The above method was tested in laboratory conditions. Experiment results show that the developed method can effectively solve the problem of the accurate extraction of structured light stripe in real time.

Copyright
© 2013, 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/).

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Journal
Journal of Robotics, Networking and Artificial Life
Volume-Issue
4 - 1
Pages
1 - 4
Publication Date
2017/06/01
ISSN (Online)
2352-6386
ISSN (Print)
2405-9021
DOI
10.2991/jrnal.2017.4.1.1How to use a DOI?
Copyright
© 2013, 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  - JOUR
AU  - Jiwu Wang
AU  - Yaodong Li
AU  - Zhijing Jian
AU  - Masanori Sugisaka
PY  - 2017
DA  - 2017/06/01
TI  - An accurate method for the extraction of line structured light stripe
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 1
EP  - 4
VL  - 4
IS  - 1
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.2017.4.1.1
DO  - 10.2991/jrnal.2017.4.1.1
ID  - Wang2017
ER  -