High dynamic range optimal fuzzy color image enhancement using Artificial Ant Colony System
Applied Soft Computing
Binarization based edge detection using universal law of gravity and ant colony optimization
International Journal of Hybrid Intelligent Systems
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A new approach for edge detection is presented in this paper using fuzzy derivative and Ant Colony Optimization (ACO) algorithm to reduce the discontinuities presented in the image filtered by Sobel operator. The number of ants are calculated and placed at the endpoints of the edges in the image filtered by Sobel Edge detector. Fuzzy Derivative Technique gives fuzzy probability factor. This probability factor is used to decide the next most probable pixel to be edge. The Ant colony optimization (ACO) technique is taken from the behavior of some species of ants which uses certain chemicals (known as pheromone) to inform other ants about the appropriate path. The intensities of the pheromones help ants for making decision for the right path. This concept is used by placing artificial ants on the image and edges are calculated by considering intensity difference as heuristic information. Two rules are also proposed for reducing movement of ant.