Particle swam optimization for image registration

This paper discusses the particle swam optimization for image registration. The term particle swarm optimization (PSO) refers to a relatively new family of algorithms that may be used to find optimal (or near optimal) solutions to numerical and qualitative problems. It is easily implemented and has proven both very effective and quick when applied to a diverse set of optimization problems. During the past several years, PSO has been successfully applied to multidimensional optimization problems, artificial neural nework training, and multiobjective optimization problems. Presently PSO technique is used for registration, which is a fundamental task in image processing that, is used to match two or more pictures taken. In this we choose a point-mapping technique because it reduces the complexity of registration algorithms, increases the precision of the optimal transformation and permits to eliminate a big number of aberrant matches. A modified Particle Swarm Optimizer, which deals with permutation problems.