Ant colony search
Ants move across the image, guided by pheromone trails that mark likely edges.
Abstract
Ant Colony Optimization (ACO) is a nature inspired meta-heuristic algorithms, which can be applied to a wide range of optimization problems. In this paper we present a modified method for edge detection based on the Ant Colony Optimization. Because of disadvantages of traditional edge detection methods, ACO as a relatively new meta-heuristic approach has been used to solve the edge detection problem. The performance of proposed method is compared with traditional ant colony methods, also we have large number of experiments to find out the suitable threshold for proposed method. The experimental results clearly indicate how the ACO can extracts edges in efficient way, also we speed up the proposed method by modifying the effective parameters in speed of the problem and replacing them by optimized values. The results show that this method is faster and more efficient than other former Ant Colony-based edge detection methods.
Method
Tuned threshold
A large set of experiments finds the suitable threshold for the proposed method.
Faster detection
Effective parameters are modified and optimized, making the method faster and more efficient than former ant colony based edge detection.
Resources
- Publisher
- IEEE Xplore (DOI)
BibTeX
@inproceedings{ayanzadeh2015modified,
title = {A modified ant colony based approach to digital image edge detection},
author = {Ayanzadeh, Aydin and Pourghaemi, Hossein and Seyfari, Yousef},
booktitle = {2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI)},
pages = {504--509},
year = {2015},
organization = {IEEE},
doi = {10.1109/KBEI.2015.7436096}
}