Strawberry Image Segmentation Based on Level Set Method
DOI:
CSTR:
Author:
Affiliation:

Clc Number:

Fund Project:

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
  • |
  • Comments
    Abstract:

    In practical applications, when the object itself contains some inherent color or texture features, these features can be used as a priori information, which can greatly improve the accuracy of the segmentation. Therefore, this paper proposes an improved level set method which is based on prior information. Firstly, we use the thought of the energy structure of C-V model to construct an energy function that contains of color information to segment color image. Then, we put the color component into the traditional structure tensor for the texture image segmentation. Finally, we get the new level set model from the traditional C-V model with the new energy structure and the penalty term of Li model. In view of the color and the texture information of strawberry fruit, the improved level set method has been applied to the segmentation of fruit image in agriculture. By experiment on laboratory and nature, the result shows that the improved can not only segment out strawberry, but also segment texture on the surface of strawberry. Comparing with the OTSU algorithm, the traditional C-V model and improved C-V model, the experimental results show that the proposed method has better segmentation result.

    Reference
    Related
    Cited by
Get Citation

朱勇军,孔斌,何立新,孙翠敏,谢成军.基于先验知识水平集方法的草莓图像分割.计算机系统应用,2016,25(2):124-129

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:May 18,2015
  • Revised:July 06,2015
  • Adopted:
  • Online: February 23,2016
  • Published:
Article QR Code
You are the firstVisitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-3
Address:4# South Fourth Street, Zhongguancun,Haidian, Beijing,Postal Code:100190
Phone:010-62661041 Fax: Email:csa (a) iscas.ac.cn
Technical Support:Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063