Referenced Items are not available for this document.
The mean shift algorithm is a statistical iterative algorithm based on kernel density estimation which has been widely used in many fields. This paper improves the mean shift algorithm by adopting the following approaches. Firstly, we present a novel approach named Random Sampling with Contexts (RSC) to speed up the mean shift algorithm. Secondly, we introduce Dempster-Shafer (D-S) theory for the fusion of features to improve the segmenting quality. Moreover, experimental results show that the new algorithm is superior to the typical mean shift algorithm.
Published in:
Computer Application and System Modeling (ICCASM), 2010 International Conference on
(Volume:6
)
Date of Conference: 22-24 Oct. 2010
- Page(s):
- V6-116 - V6-120
- E-ISBN :
- 978-1-4244-7237-6
- Print ISBN:
- 978-1-4244-7235-2
- INSPEC Accession Number:
- 11637287
- Conference Location :
- Taiyuan
- Digital Object Identifier :
- 10.1109/ICCASM.2010.5618989
- Product Type:
- Conference Publications
About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2013 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.
