A novel particle swarm optimization-based quantum algorithm for machine vision under complicated background

In recent years, machine vision has played a more and more important role in the fields of industry, medicine, security and all kinds of other situations where automatic monitoring is needed. The central part of machine vision is image matching which requires both high accuracy and effectiveness. The conventional intensify-based matching approach has the advantage of high accuracy yet lacks the time efficiency needed. In this paper, a new intelligent algorithm is developed to optimize the conventional intensify-based image matching process. This algorithm comes from the combination of Quantum Algorithm (QA) and Particle Swarm Optimization (PSO). Experiments showed that the approach received the advantages of both QA and PSO. The results in the work showed that the Particle Swarm Optimization-based Quantum Algorithm (PSO-QA) method is a feasible and effective method for achieving image matching process in machine vision field.