期刊名称：International Journal of Computer Science Issues
印刷版ISSN：1694-0784
电子版ISSN：1694-0814
出版年度：2013
卷号：10
期号：5
出版社：IJCSI Press
摘要：Object recognition is an important research field of computer vision and has its application in a broad range of problems including image retrieval, compression, surveillance and medical diagnostics. The main goal of the object recognition problem is to recognize the objects of the same type even when they are viewed from different viewpoints. This goal, however, remains a challenge for computer vision to recognize objects having invariant features such as translation, rotation and scaling. Shape descriptors like Fourier and Moments are invariant with respect to transformation, rotation and scaling. Particle swarm optimization (PSO) is a population based soft computing technique. Particle Swarm Optimization technique shares numerous similarities with evolutionary computation techniques such as Genetic Algorithms (GA). One of the most important tasks regarding to object recognition is how to find number of descriptors of a given object. The query that arises is what is the optimum number of descriptors to be used with maximum recognition rate? , Are descriptors having equal importance? Such reasons signify the importance of these descriptors and also selecting the best descriptor by applying optimization technique. We have introduced an evolutionary optimization technique known as Genetic algorithm (GA) for solving the optimization problem. GA assigns, for each of these descriptors, a weighting factor that reflects the relative importance of that descriptor.
