Cart (Loading....) | Create Account
Close category search window

Adaptive thresholds edge detection for defective parts images based on wavelet transform

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Jing Li ; Sch. of Electron. & Inf. Eng., Xi''an Technol. Univ., Xi''an, China ; Zhiyong Lei

Image edge detection plays an important role in the system of computer vision. Wavelet is a powerful tool in image processing and has wide application to edge detection for its multiscale characteristic. Based on wavelet modulus maximum edge detection algorithm, an improved method is proposed in this paper, which gives an automatic determination function of eliminating noise threshold by using the clustering technique. Some experiments were made using B-spline wavelet and improved K-means clustering algorithm. The experimental results show that this method is correct and effective to defective parts, and the result was better than that using fixed thresholds.

Published in:

Electric Information and Control Engineering (ICEICE), 2011 International Conference on

Date of Conference:

15-17 April 2011

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.