Scheduled System Maintenance on May 29th, 2015:
IEEE Xplore will be upgraded between 11:00 AM and 10:00 PM EDT. During this time there may be intermittent impact on performance. For technical support, please contact us at onlinesupport@ieee.org. We apologize for any inconvenience.
By Topic

A three-module strategy for edge detection

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

1 Author(s)
Lacroix, V. ; Philips Res. Lab., Brussels, Belgium

The first module is a parallel process computing local edge strength and direction, while the last module is sequential process following edges. The originality of the overall method resides in the intermediate module, which is seen as a generalization of the nonmaximum-deletion algorithm. The role of this module is twofold: It enables one to postpone some deletion to the last module where contextual information is available, and it transmits the local edge direction in order to guide the contour following. A postprocessing method called learning edges is proposed as a refinement of the method. The binary edge images extracted from various gray-level images illustrate the power of the strategy

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:10 ,  Issue: 6 )