A Comprehensive Study of Edge Detection Techniques in Image Processing Applications Using Particle Swarm Optimization Algorithm

Edge detection is an important task in image processing. Edge is defined as the boundary between two regions separated by two relatively distinct gray level properties. Traditional edge detection methods give rise to the exponential increment of computational time. In this paper, edge detection in gray level images is done by using Renyi entropy and particle swarm optimization (PSO) algorithm. The Renyi entropy is a one-parameter generalization of the Shannon entropy. Here Renyi entropy was calculated for the one-dimensional histogram of the images. PSO is an efficient and powerful populationbased stochastic search technique for solving optimization problems, and this has been widely applicable in many scientific and engineering fields. The selection of the initial population in a population-based heuristic optimization method is most important, as it affects the search for a number of iterations and has an influence on the final solution. If the prior information about the optima is not available, then the initial population is selected randomly using a pseudorandom numbers. The main advantage of PSO algorithm is its simple in structure, easy to use, speed and robustness.