Segmentation by Elastica Energy with L^(1) and L^(2) Curvatures: a Performance Comparison  

作　　者：Xuan He Wei Zhu Xue-Cheng Tai 

机构地区：[1]Department of Mathematics,University of Alabama,Box 870350,Tuscaloosa,AL 35487,USA [2]Department of Mathematics,Hong Kong Baptist University,KowloonTong Kowloon,Hong Kong

出　　处：《Numerical Mathematics(Theory,Methods and Applications)》2019年第1期285-311,共27页高等学校计算数学学报（英文版）

基　　金：X.C.Tai was supported by the startup grant at Hong Kong Baptist University,grant RG(R)-RC/17-18/02-MATH and FRG2/17-18/033.

摘　　要：In this paper,we propose an algorithm based on augmented Lagrangian method and give a performance comparison for two segmentation models that use the L^(1)-and L^(2)-Euler’s elastica energy respectively as the regularization for image seg-mentation.To capture contour curvature more reliably,we develop novel augmented Lagrangian functionals that ensure the segmentation level set function to be signed dis-tance functions,which avoids the reinitialization of segmentation function during the iterative process.With the proposed algorithm and with the same initial contours,we compare the performance of these two high-order segmentation models and numerically verify the different properties of the two models.

关 键 词：Augmented Lagrangian method Euler’s elastica image segmentation 

分 类 号：O17[理学—数学]