Scheduled System Maintenance:
On May 6th, single article purchases and IEEE account management will be unavailable from 8:00 AM - 12:00 PM ET (12:00 - 16:00 UTC). We apologize for the inconvenience.
By Topic

Geometrical constraints for object recognition using genetic algorithms

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)
Bai Li ; Sch. of Comput. Sci. & Inf. Technol., Nottingham Univ., UK ; Elliman, D.

In this paper we describe how different types of constraints can affect the performance of genetic algorithms (GAs). The success of a GA application depends on efficient constraints to provide a clear direction for GA search. Our application integrates geometrical constraints with a GA to guide the pattern matching process for image registration and object location. Two types of constraints, namely, local and global constraints are considered Although both types of constraints are useful in constraining the pattern matching search space, the former type is less effective than the latter type. Intuitively one would expect that the combination of both types of constraints should be more powerful than each of them used alone. Yet our experimental result proves to the contrary. We describe the whole process of integrating geometrical constraints with a GA for pattern matching and analyse the results

Published in:

Information Intelligence and Systems, 1999. Proceedings. 1999 International Conference on

Date of Conference:

1999