AASC: Advanced Ant Based Swarm Computing for Detection of Edges in Imagery
Rebika Rai, Ratika Pradhan, M. K. Ghose · 2013
The social insect allegory for working out problems has become a promising area in the recent years emphasizing on stochastic construction practice building the key probabilistically. The approach focuses on direct or indirect communications among uncomplicated agents. Swarm Intelligence is the collective behavior of decentralized, self-organized system whereby the joint behavior of agent interacting locally with the environment causes coherent global pattern to emerge. Ant Colony Optimization (ACO) is an algorithm inspired by the foraging behavior of ants wherein ants leaves a volatile substance call pheromone on the soil surface for the purpose of foraging and collective interaction via indirect communication. Edge detection mainly is the set of mathematically methods aiming to identify points in an image at which the image brightness changes sharply or formally generating some formation of discontinuities. This paper explores the Swarm computing technique called Ant Colony Optimization (ACO) and further proposes a new technique called as Advanced Ant based Swarm Computing (AASC) for edge detection of imagery.