We use cookies to ensure that we give you the best experience on our website. By continuing to browse this repository, you give consent for essential cookies to be used. You can read more about our Privacy and Cookie Policy.

Durham Research Online
You are in:

Point set denoising using a variational Bayesian method.

Yoon, Mincheol and Ivrissimtzis, Ioannis (2008) 'Point set denoising using a variational Bayesian method.', Journal of KISS : computing practices and letters., 14 (5). pp. 527-531.


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.

Item Type:Article
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 Science, Software Engineering.
Full text:Full text not available from this repository.
Publisher Web site:
Date accepted:No date available
Date deposited:No date available
Date of first online publication:2008
Date first made open access:No date available

Save or Share this output

Look up in GoogleScholar