Skip to main content
Conference ProceedingsOPEN ACCESS

An improved ant colony matching by using discrete curve evolution

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2014) 8407 LNCS 248-256
DOI: 10.1007/978-3-642-55032-4_24
0Citations
Citations of this article
2Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

In this paper we present an improved Ant Colony Optimization (ACO) for contour matching, which can be used to match 2D shapes. Discrete Curve Evolution (DCE) technique is used to simplify the extracted contour. In order to find the best correspondence between shapes, the match process is formulated as a Quadratic Assignment Problem (QAP) and resolved by using Ant Colony Optimization (ACO). The experimental results justify that Discrete Curve Evolution (DCE) performs better than the previous Constant Sampling (CS) technique which has been selected for the ACO matching. © 2014 IFIP International Federation for Information Processing.

Author supplied keywords

Cite

CITATION STYLE

APA

Saadi, Y., Sari, E. N., & Herawan, T. (2014). An improved ant colony matching by using discrete curve evolution. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8407 LNCS, pp. 248–256). Springer Verlag. https://doi.org/10.1007/978-3-642-55032-4_24

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free