Abstract
Robust filters are those filters that are not influenced to a large extent by outliers in profiles. We describe the adaptation of the Gaussian filter in the form of a two-pass method that is modestly robust against outliers. This method, described in international Standards, is referred to as the Rk method. We consider an example profile with outliers that is used to demonstrate how the Rk filter is less influenced by outliers than the Gaussian filter. We also describe a Gaussian regression based robust filter that employs a multi-pass method that is robust against outliers. We compare this method against the Rk method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Brinkmann, S., Bodschwinna, H. and Lemke, H.W. 2000, ‘Development of a robust Gaussian regression filter for three-dimensional surface analysis’, Proceedings of the X International Colloquium on Surfaces, Chemnitz University of Technology. Chemnitz, pp. 122–132.
Deutsches Institut für Normung 1990, DIN 4776:1990, Measurement of Surface Roughness – Parameters R k ,R pk ,R vk ,M r1,M r2 for Describing the Material Portion in the Roughness Profile, DIN.
Goto, T., Miyakura, J., Umeda, K., Kadowaki, S. and Yanagi, K. 2005, ‘A robust spline filter on the basis of L2-norm’, Precision Engineering, vol. 29, no. 2, pp. 157–161.
International Organization for Standardization 1996, ISO 13565-1:1996 (E), Geometrical Product Specifications (GPS) – Surface Texture: Profile Method; Surfaces Having Stratified Functional Properties – Part 1: Filtering and General Measurement Condition, ISO. Geneva, Switzerland.
Rights and permissions
Copyright information
© 2009 Springer London
About this chapter
Cite this chapter
(2009). Robust Filters. In: Computational Surface and Roundness Metrology. Springer, London. https://doi.org/10.1007/978-1-84800-297-5_11
Download citation
DOI: https://doi.org/10.1007/978-1-84800-297-5_11
Publisher Name: Springer, London
Print ISBN: 978-1-84800-296-8
Online ISBN: 978-1-84800-297-5
eBook Packages: EngineeringEngineering (R0)