원문정보
보안공학연구지원센터(IJSIP)
International Journal of Signal Processing, Image Processing and Pattern Recognition
Vol.6 No.5
2013.10
pp.13-24
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
저자정보
참고문헌
자료제공 : 네이버학술정보