Thresholding Based on Improved 2D Otsu Method and Chaotic Particle Swarm Optimization
改进的2维Otsu法及混沌粒子群递推的阈值分割

In view of the shortage of regional division of the commonly used gray level-average gray level two-dimensional histogram, which some object and background inner points are wrongly divided as edge and noise points, an improved Otsu threshold selection method based on gray level-gradient two-dimensional histogram is proposed in this paper. The chaotic particle swarm algorithm is used to search for the best threshold. The repeat computations of the fitness function in iteration are reduced significantly using recursion. Compared with fast image segmentation algorithm based on gray level-average gray level 2-D Otsu method and particle swarm optimization , the experimental results show that the algorithm proposed in this paper not only considers all the object and background inner points and achieves a good segmentation quality in uniform regions, accurate borders and clear details of features,but also the running time is reduced to only 1/3 of that of the existing algorithm. At the same time the convergence property of particle swarm algorithm is further improved.