A NEW VARIATIONAL APPROACH BASED ON LEVEL-SET FUNCTION FOR CONVEX HULL PROBLEM WITH OUTLIERS

被引:4
|
作者
Li, Lingfeng [1 ,2 ]
Luo, Shousheng [3 ,4 ]
Tai, Xue-Cheng [1 ]
Yang, Jiang [2 ]
机构
[1] Hong Kong Baptist Univ, Dept Math, Hong Kong, Peoples R China
[2] Southern Univ Sci & Technol, Dept Math, Shenzhen, Peoples R China
[3] Henan Univ, Sch Math & Stat, Data Anal Technol Lab, Kaifeng, Peoples R China
[4] Henan Engn Res Ctr Artificial Intelligence Theory, Kaifeng, Peoples R China
关键词
Convex hull; Level-set method; Variational method; ADMM; Outliers; ALGORITHMS; SEGMENTATION; MODEL; APPROXIMATION; RECOGNITION; FRAMEWORK;
D O I
10.3934/ipi.2020070
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
Seeking the convex hull of an object (or point set is a very fundamental problem arising from various tasks. In this work, we propose a variational approach based on the level-set representation for convex hulls of 2-dimensional objects. This method can adapt to exact and inexact convex hull problems. In addition, this method can compute multiple convex hulls simultaneously. In this model, the convex hull is characterized by the zero sublevel-set of a level-set function. For the exact case, we require the zero sublevel-set to be convex and contain the whole given object, where the convexity is characterized by the non-negativity of Laplacian of the level-set function. Then, the convex hull can be obtained by minimizing the area of the zero sublevel-set. For the inexact case, instead of requiring all the given points are included, we penalize the distance from all given points to the zero sublevel-set. Especially, the inexact model can handle the convex hull problem of the given set with outliers very well, while most of the existing methods fail. An efficient numerical scheme using the alternating direction method of multipliers is developed. Numerical examples are given to demonstrate the advantages of the proposed methods.
引用
收藏
页码:315 / 338
页数:24
相关论文
共 50 条
  • [21] Variational level-set reconstruction of accretionary morphogenesis from images
    Fablet, R.
    Pujolle, S.
    Chessel, A.
    Benzinou, A.
    Cao, F.
    2006 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP 2006, PROCEEDINGS, 2006, : 221 - +
  • [22] New Level-Set Based Algorithm for Bimodal Depth Segmentation
    Krumnikl, Michal
    Sojka, Eduard
    Gaura, Jan
    ADVANCED CONCEPTS FOR INTELLIGENT VISION SYSTEMS (ACIVS 2012), 2012, 7517 : 225 - 236
  • [23] A unified variational segmentation framework with a level-set based sparse composite shape prior
    Liu, Wenyang
    Ruan, Dan
    PHYSICS IN MEDICINE AND BIOLOGY, 2015, 60 (05): : 1865 - 1877
  • [24] Metamorphosis based on the level-set methods
    Pan, Qing
    Xu, Guo-Liang
    Jisuanji Xuebao/Chinese Journal of Computers, 2009, 32 (02): : 213 - 220
  • [25] A level-set approach for the metamorphosis of solid models
    Breen, DE
    Whitaker, RT
    IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, 2001, 7 (02) : 173 - 192
  • [26] A multiresolution level-set approach to surface fusion
    Sarti, A
    Tubaro, S
    2001 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL II, PROCEEDINGS, 2001, : 205 - 208
  • [27] A convex variational level set model for image segmentation
    Wu, Yongfei
    He, Chuanjiang
    SIGNAL PROCESSING, 2015, 106 : 123 - 133
  • [28] Buckling-based topology optimization for underwater pressure hull with modified parameterized level-set method
    Jiang, Yuanteng
    He, Tengwu
    Zhao, Min
    EUROPEAN JOURNAL OF MECHANICS A-SOLIDS, 2025, 110
  • [29] Level-Set Methods for Finite-Sum Constrained Convex Optimization
    Lin, Qihang
    Ma, Runchao
    Yang, Tianbao
    INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 80, 2018, 80
  • [30] A variational binary level-set method for elliptic shape optimization problems
    Zhu, Shengfeng
    Dai, Xiaoxia
    Liu, Chunxiao
    INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS, 2011, 88 (14) : 3026 - 3045