Paper Title
Intelligent Control Based Multilevel And Multiband Digital Image Thresholding Using Fuzzy Entropy And Particle Swarm Optimization

Thresholding is a fundamental image segmentation approach which transforms grayscale images into binary images. For more complicated cases than bi-level thresholding, extension is necessary in order to fulfill multilevel and multiband image thresholding. In multilevel thresholding, computation complexity increases significantly. In multilband thresholding, multiple dimension optimization should be made across different color frequency bands. Balanced histogram thresholding is widely accomplished in automatic image thresholding. Fuzzy entropy is thus introduced as the objective function to solve the optimal thresholds, where the image histogram is partitioned into various objects via fuzzy partition, so that diverse fuzzy membership functions can be assigned. Fuzzy entropy based optimization has been formulated accordingly. To speed up the convergence rate and shorten the computation time, particle swarm optimization based intelligent control schemes have been proposed to optimize thresholds rapidly. Comparisons of the role of fuzzy membership assignment on the thresholding quality are also made. Superior outcomes on multilevel and multiband thresholding are reached. Index Terms— Fuzzy Entropy, Fuzzy Membership Function, Particle Swarm Optimization, Intelligent Control, Multilevel Thresholding, Multiband Thresholding