A Variational Convex Hull Algorithm

Lingfeng Li, Shousheng Luo, Xue-Cheng TAI*, Jiang Yang

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Finding the convex hull of a given object or a point set is a very important problem. In this paper, we propose a variational convex hull model and numerical algorithms to solve it. Our model is based on level set representation. Efficient numerical algorithms and implementations based on splitting ideas are given. To test our proposed model, we conduct many experiments for objects represented by binary images, and the results suggest that our model can identify the convex hull accurately. Even more, our model can be easily modified to handle the outliers, and this is also demonstrated by numerical examples.

Original languageEnglish
Title of host publicationScale Space and Variational Methods in Computer Vision - 7th International Conference, SSVM 2019, Proceedings
EditorsJan Lellmann, Jan Modersitzki, Martin Burger
PublisherSpringer Verlag
Pages224-235
Number of pages12
ISBN (Print)9783030223670
DOIs
Publication statusPublished - 2019
Event7th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2019 - Hofgeismar, Germany
Duration: 30 Jun 20194 Jul 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11603 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2019
Country/TerritoryGermany
CityHofgeismar
Period30/06/194/07/19

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • Convex hull
  • Image processing
  • Level set method
  • Outliers detection

Fingerprint

Dive into the research topics of 'A Variational Convex Hull Algorithm'. Together they form a unique fingerprint.

Cite this