Image Thresholding Based on 2-D oblique Exponent Entropy Method and  Tent Map Chaotic Particle Swarm Algorithm

In view of the inefficient shortage of computational efficiency or convergence precision of the existing thresholding methods based on 2-D exponent entropy, an improved 2-D oblique exponent entropy method based on tent map chaotic particle swarm algorithm is proposed in this paper. To achieve higher segmented accuracy and stronger anti-noise, an oblique regional division mode for histogram based thresholding method is introduced firstly, and then the chaotic particle swarm algorithm based on improved tent map is used to search for the optimal threshold so as to improve the convergence precision and computational efficiency. Compared with the fast thresholding method based on gray scaleaverage gray scale histogram of vertical regional division, experimental results show that the new method achieves a better segmentation quality since the entire object and background inner points are considered. And because the searching process of the new method is optimized by using chaotic particle swarm algorithm, the running time is reduced. Compared with the method based on gray scale-gradient histogram and logistic map chaotic particle swarm algorithm, a stronger anti-noise performance and higher convergence precision are obtained.