Earticle

초록

영어

Thresholding based on variance analysis of gray levels histogram is a very effective technology for image segmentation. However, its performance is limited in conventional forms. In this paper, a novel method based on two-dimensional extension of within-class variance is proposed to improve segmentation performance. The two-dimensional histogram of the original and local average image is projected to one-dimensional space firstly, and then the minimum within-class variance criterion is constructed for threshold selection. The effectiveness of the proposed method is demonstrated by using examples from the synthetic and real-word images.

목차

Abstract
 1. Introduction
 2. Conventional Variance-based Thresholding Method
 3. The Proposed Method
 4. Performance Evaluation and Experiments
  4.1. Evaluation of the Performance based on Synthetic Images
  4.2. Experiments on Real Images
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Fangyan Nie [ College of Computer Science & Technology, Hunan University of Arts and Science, Changde Hunan 415000, China ]
• Jianqi Li [ Discipline Development Office, Hunan University of Arts and Science, Changde Hunan 415000, China ]
• Tianyi Tu [ College of Computer Science & Technology, Hunan University of Arts and Science, Changde Hunan 415000, China ]
• Pingfeng Zhang [ College of Computer Science & Technology, Hunan University of Arts and Science, Changde Hunan 415000, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605