Open AccessA Hybrid Approach of Ant Colony Optimization Technique to Detect Edges of an Image
Author(s) -
Kamna
Publication year - 2019
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit195450
Subject(s) - pixel , artificial intelligence , ant colony optimization algorithms , enhanced data rates for gsm evolution , computer science , edge detection , computer vision , image (mathematics) , boundary (topology) , matrix (chemical analysis) , pattern recognition (psychology) , image processing , mathematics , mathematical analysis , materials science , composite material
An edge is a collection of linked pixels lying between boundaries of two regions. It is a local concept but the boundary of an edge is a universal concept. An ideal edge is a group of pixels located at an orthogonal step transition in gray level. Blurry edges are also acquired by the factors like problems or imperfections happened at the time of optics, sampling and image acquisition systems etc. So, edges can be closely seen as having a profile as that of ramp-like profile. Ant colony optimization is an algorithm which is inspired by the natural foraging behavior and activities of ants. ACO is mainly introduced here to tackle the image edge detection problem. The proposed approach generates a matrix, called as pheromone matrix that represents the edge information which is stored at each pixel according to the movement of ants. The movements of these ants can be determined by local changes in the intensity value of pixel.