Technology of Graphic & Image
|
App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 6
1916-1920

Level set image segmentation model based on total Bregman divergence

Li Honglei1
Wang Yi2
1. Artificial Intelligence & Big Data College, Chongqing College of Electronic Engineering, Chongqing 401331, China
2. School of Computer Science, Chongqing University, Chongqing 400044, China
DOI: 10.19734/j.issn.1001-3695.2019.01.0027

Abstract

Image segmentation plays an important role in the research field of computer vision and it is the foundation of high-level semantic analysis. However, image segmentation is a very challenging problem due to the wide range of image sources and the complexity of imaging conditions. Focusing on the problems that the traditional active contour models are not fully suitable for image segmentation with noise and weak edge, this paper proposed a global optimal segmentation method based on TBD. Firstly, the TBD replaced the original l2 measure in the traditional models to construct the segmentation energy functional. Then it applied a solution to obtain the global optimum alternately. The optimal solution represented the final target boundaries. Finally, experimental results on synthetic, medical and nature images validated the high robustness and noise immunity of the proposed model. It can achieve the object boundaries in noise and weak edge accurately and efficiently.

Foundation Support

国家自然科学基金资助项目(61672120)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.01.0027
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 6
Section: Technology of Graphic & Image
Pages: 1916-1920
Serial Number: 1001-3695(2020)06-065-1916-05

Publish History

[2020-06-05] Printed Article

Cite This Article

李红蕾, 王翊. Bregman全散度水平集图像分割方法 [J]. 计算机应用研究, 2020, 37 (6): 1916-1920. (Li Honglei, Wang Yi. Level set image segmentation model based on total Bregman divergence [J]. Application Research of Computers, 2020, 37 (6): 1916-1920. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)