By Topic

A constrained regularization approach to robust corner 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
$33 $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

4 Author(s)
Kwanghoon Sohn ; Satellite Commun. Div., Electron. and Telecom. Res. Inst., Daejeon, South Korea ; W. E. Alexander ; J. H. Kim ; W. E. Snyder

This paper presents a method of optimal boundary smoothing for curvature estimation and a method of corner detection for consistent representation of objects for computer vision applications. The existing methods for curvature estimation have a common problem in determining a unique smoothing factor. We propose a constrained regularization (CR) approach to overcome that problem. The curvature function computed on the preprocessed boundary, which is obtained by the CR approach, gives consistent corner detection results. Ideal corners rarely exist for a real boundary. They are often rounded due to the smoothing effects of the preprocessing. In addition, a human recognizes both sharp corners and slightly rounded segments as corners. Hence, we establish a criterion, called “corner sharpness”, which is qualitatively similar to a human's capability to detect corners

Published in:

IEEE Transactions on Systems, Man, and Cybernetics  (Volume:24 ,  Issue: 5 )