Double regularization control based on level set evolution for tablet packaging image segmentation

作     者：Li Liu Ao-Lei Yang Xiao-Wei Tu Wen-Ju Zhou Min-Rui Fei Jun Yue 

作者机构：Shanghai Key Laboratory of Power Station Automation TechnologySchool of Mechatronic Engineering and AutomationShanghai University School of Information Science and Electrical EngineeringLudong University School of Computer Science and Electronic EngineeringUniversity of Essex.Colchester CO4 3SQUK 

出 版 物：《Advances in Manufacturing》 (先进制造进展（英文版）)

年 卷 期：2015年第3卷第1期

页      面：73-83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] 081201[工学-计算机系统结构] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：the Science and Technology Commission of Shanghai Municipality(Grant No.14YF1408600) the Shanghai Municipal Commission of Economy and Informatization under Shanghai Industry University Research Collaboration(Grant No.CXY-2013-71) the Natural Science Foundation of Shandong Province(Grant No.ZR2012FM008) the Science and Technology Development Program of Shandong Province(Grant No.2013GNC11012) the National Natural Science Foundation of China(Grant No.61100115) 

主　　题：Tablet packaging image . Level setevolution   Image segmentation   Curvatures . Doubleregularization control (DRC) 

摘      要：This paper proposes a novel double regular- ization control （DRC） method which is used for tablet packaging image segmentation. Since the intensities of tablet packaging images are inhomogenous, it is difficult to make image segmentation. Compared to methods based on level set, the proposed DRC method has some advantages for tablet packaging image segmentation. The local re- gional control term and the rectangle initialization contour are first employed in this method to quickly segment un- even grayscale images and accelerate the curve evolution rate. Gaussian filter operator and the convolution calcula- tion are then adopted to remove the effects of texture noises in image segmentation. The developed penalty energy function, as regularization term, increases the constrained conditions based on the gradient flow conditions. Since the potential function is embedded into the level set of evo- lution equations and the image contour evolutions are bi- laterally extended, the proposed method further improves the accuracy of image contours. Experimental studies show that the DRC method greatly improves the computational efficiency and numerical accuracy, and achieves better results for image contour segmentation compared to other level set methods.