Study And Application Of Image Thresholding Method Based On PSO Algorithm

In applications of digital image processing, the extraction of image objects is needed in many cases, such as face recognition, text recognition, fingerprint recognition, license plate recognition and content-based image retrieval. Image segmentation is a crucial step in preprocessing of image recognition. Image threshold technology is the most commonly used method of image segmentation because its principle is very simple and easy to implement. In image segmentation algorithms, the selection of optimal threshold is the key to segmentation. However, the most of threshold selection methods adopt the mode of exhaustive search so that the operation efficiency is low, the capability of noise resisting is weak, and error segmentation happens easily in these methods.To solve the above problems, this paper adopts intelligent optimization algorithms to search for the optimal threshold, aiming to maximize the efficiency and accuracy. Particle swarm optimization (PSO) algorithm is a new intelligent one with simple principle which is easy to implement. Based on the study on PSO algorithm, a new algorithm to select optimal threshold is proposed and then implemented in the application field of image segmentation.The first step of image threshold method discussed in this paper is image denoising and making two-dimensional histogram of the image. The second one is to select appropriate values of gray level as initial population according to the two-dimensional histograms. It can reduce the computation burden and improve efficiency of algorithm. The last one is to iterate using evolution equation containing dynamic inertia weight. In order to get appropriate inertia weight for different data, it needs to adjust the parameters of dynamic inertia weight. Finally, the output of this algorithm is the optimal threshold. Using this threshold to partition off the pixels, image segmentation is implemented.In this paper, a new idea is adding dynamic inertia weight in evolution equation of PSO algorithm, a method of image threshold DPSO (Dynamic PSO) is proposed. It makes the proportion of the local and global searching ability can be effectively controlled in the whole process of optimal searching. This paper applies DPSO algorithm to pretreatment of face recognition. Gray image about skin probability is processed by threshold. According to the color of skin, image containing faces is segmented, and the binary image can be got based on skin-color. The data of experiment shows that the algorithm can separate skin and non-skin regions accurately, and then the result of segmentation is better than method using two- dimensional maximum entropy. These results show DPSO image threshold method is effective and valuable.