![](/images/graphics-bg.png)
The Segmentation of Wear Particles Images Using J -Segmentation Algorithm
Joint Authors
Liu, Hong
Wei, Haijun
Wei, Lidui
Li, Jingming
Yang, Zhiyuan
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-04-18
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
This study aims to use a JSEG algorithm to segment the wear particle’s image.
Wear particles provide detailed information about the wear processes taking place between mechanical components.
Autosegmentation of their images is key to intelligent classification system.
This study examined whether this algorithm can be used in particles’ image segmentation.
Different scales have been tested.
Compared with traditional thresholding along with edge detector, the JSEG algorithm showed promising result.
It offers a relatively higher accuracy and can be used on color image instead of gray image with little computing complexity.
A conclusion can be drawn that the JSEG method is suited for imaged wear particle segmentation and can be put into practical use in wear particle’s identification system.
American Psychological Association (APA)
Liu, Hong& Wei, Haijun& Wei, Lidui& Li, Jingming& Yang, Zhiyuan. 2016. The Segmentation of Wear Particles Images Using J -Segmentation Algorithm. Advances in Tribology،Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1096724
Modern Language Association (MLA)
Liu, Hong…[et al.]. The Segmentation of Wear Particles Images Using J -Segmentation Algorithm. Advances in Tribology No. 2016 (2016), pp.1-10.
https://search.emarefa.net/detail/BIM-1096724
American Medical Association (AMA)
Liu, Hong& Wei, Haijun& Wei, Lidui& Li, Jingming& Yang, Zhiyuan. The Segmentation of Wear Particles Images Using J -Segmentation Algorithm. Advances in Tribology. 2016. Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1096724
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1096724