Anisotropic adaptive method for triangular meshes smoothing. Issue 6 (1st June 2015)

• Record Type: Journal Article
• Authors: El Ouafdi, Ahmed Fouad; El Houari, Hassan; Ziou, Djemel
• Is Part Of: IET image processing. Volume 9:Issue 6(2015)
• Page Start: 516
• Page End: 526
• Publication Date: 2015-06-01
• Subjects: optical scanners -- tensors -- statistical distributions -- filtering theory -- image denoising; anisotropic adaptive method -- triangular mesh smoothing -- advancing laser scanner technology -- digital objects -- random noise -- acquisition process -- anisotropic filtering -- variation calculus -- mesh attribute smoothing -- normal vector -- vertex positions -- statistical distribution noise -- anisotropic mesh smoothing -- probability density function -- anisotropic smoothing operation -- diffusion tensor -- real‐time noise reduction application; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2014.0447
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Even with the advancing laser scanner technology, the digital objects are inevitably corrupted with random noise during the acquisition process. Many algorithms, regardless of principle, share the same basic idea of noise reduction through mesh smoothing. Smoothing can be performed locally, as in the anisotropic filtering by calculus of variations; or by smoothing mesh attributes such as normal vector and then adjusting vertex positions. In this study, the authors propose a novel algorithm based on the statistical distribution noise for anisotropic mesh smoothing. In this approach, the probability density function of the noise is first estimated, and then the anisotropic smoothing operation is performed through the diffusion tensor that allows to preserve mesh characteristics like edges, corner and ridges. The proposed algorithm is easy to implement and is thus suitable for real‐time noise reduction application.