Mincheol Yoon
Point Set Denoising using a Variational Bayesian Method
Yoon, Mincheol; Ivrissimtzis, Ioannis
Abstract
For statistical modeling, the model parameters are usually estimated by maximizing a probability measure, such as the likelihood or the posterior. In contrast, a variational Bayesian method threats the parameters of the model as probability distributions and computes optimal distributions for them rather than values. It has been shown that this approach effectively avoids the overfitting problem, which is common with other parameter optimization methods. This paper applies a variational Bayesian technique to surface fitting for height field data. Then, we propose point cloud denoising based on the basic surface fitting technique. Validation experiments and further tests with scan data verify the robustness of the proposed method.
Citation
Yoon, M., & Ivrissimtzis, I. (2008). Point Set Denoising using a Variational Bayesian Method. Jeongbo gwahaghoe nonmunji. keompyuting ui silje, 14(5), 527-531
Journal Article Type | Article |
---|---|
Publication Date | Jan 1, 2008 |
Deposit Date | Sep 28, 2010 |
Journal | 정보과학회논문지 : 컴퓨팅의 실제 |
Print ISSN | 1229-7712 |
Publisher | Korean Institute of Information Scientists and Engineers |
Peer Reviewed | Peer Reviewed |
Volume | 14 |
Issue | 5 |
Pages | 527-531 |
Keywords | Variational Bayesian method, Point set denoising, Overfitting control, Height field fitting, Computer Science, Artificial Intelligence,Computer Science, Cybernetics,Computer Science, Hardware &architecture,Computer Science, Information Systems,Computer Sc |
Publisher URL | http://ksci.kisti.re.kr/browse/browResult.ksci?browseBean.issSeq=JBGHIF_2008_v14n5 |
You might also like
Bivariate non-uniform subdivision schemes based on L-systems
(2023)
Journal Article
Big data for human security: The case of COVID-19
(2022)
Journal Article
Quality perception and discrimination thresholds in quantised triangle meshes
(2021)
Conference Proceeding
From Farey fractions to the Klein quartic and beyond
(2021)
Journal Article
Early Fault Diagnostic System for Rolling Bearing Faults in Wind Turbines
(2021)
Journal Article
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search