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Edge detection in digital images using Ant Colony Optimization
Authors: Marjan Kuchaki Rafsanjani, Zahra Asghari Varzaneh
Keywords: Ant Colony Optimization (ACO), Digital image processing, Edge detection, Noisy images
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Keywords: Ant Colony Optimization (ACO), Digital image processing, Edge detection, Noisy images
Abstract
Ant Colony Optimization (ACO) is an optimization algorithm inspired by the behavior of real ant colonies to approximate the solutions of difficult optimization problems. In this paper, ACO is introduced to tackle the image edge detection problem. The proposed approach is based on the distribution of ants on an image; ants try to find possible edges by using a state transition function. Experimental results show that the proposed method compared to standard edge detectors is less sensitive to Gaussian noise and gives finer details and thinner edges when compared to earlier ant-based approaches.Department of Computer Science,
Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran
E-mail: ,
Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran
E-mail: ,
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
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Contents
- Convex graph covers
- Set-theoretic Analysis of Nominative Data
- Admissibility, compatibility, and deducibility in first-order sequent logics
- Small P Systems with Catalysts or Anti-Matter Simulating Generalized Register Machines and Generalized Counter Automata
- Extensionality, Proper Classes, and Quantum Non-Individuality
- Edge detection in digital images using Ant Colony Optimization
- Probability on groups and an application to cryptography
- Classification of Early Stages of NAFLD Based on Dual Diagnostic Methods
- Annotation on PhD Thesis
