Entropy-Based Global and Local Weight Adaptive Image Segmentation Models

作     者：Gang Li Yi Zhao Ling Zhang Xingwei Wang Yueqin Zhang Fayun Guo Gang Li;Yi Zhao;Ling Zhang;Xingwei Wang;Yueqin Zhang;Fayun Guo

作者机构：Taiyuan University of TechnologyTaiyuan 030024China College of Computer Science and EngineeringNortheastern UniversityShengyang 110819China Shanxi Tizones Technology Co.Ltd.Taiyuan 030082China Fayun Guo is with Shanxi Taisen Technology Co.Ltd.Taiyuan 030082China 

出 版 物：《Tsinghua Science and Technology》 (清华大学学报（自然科学版（英文版）)

年 卷 期：2020年第25卷第1期

页      面：149-160页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：supported by the National Natural Science Foundation of China(No.61876124) 

主　　题：image local entropy parameter adaption image segmentation active contour 

摘      要：This paper proposes a parameter adaptive hybrid model for image segmentation. The hybrid model combines the global and local information in an image, and provides an automated solution for adjusting the selection of the two weight parameters. Firstly, it combines an improved local model with the global Chan-Vese(CV) model, while the image’s local entropy is used to establish the index for measuring the image’s gray-level information. Parameter adjustment is then performed by the real-time acquisition of the ratio of the different functional energy in a self-adapting model responsive to gray-scale distribution in the image segmentation *** with the traditional linear adjustment model, which is based on trial-and-error, this paper presents a more quantitative and intelligent method for achieving the dynamic nonlinear adjustment of global and local *** show that the proposed model achieves fast and accurate segmentation for different types of noisy and non-uniform grayscale images and noise images. Moreover, the method demonstrates high stability and is insensitive to the position of the initial contour.