Gaussian process machine learning-based surface extrapolation method for improvement of the edge effect in surface filtering

Ming Yu Liu, Chi Fai Cheung, Xiaobing Feng, Lai Ting Ho, Shu Ming Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)


Filtering for signal and data is an important technology to reduce and/or remove noise signal for further extraction of desired information. However, it is well known that significant distortions may occur in the boundary areas of the filtered data because there is no sufficient data to be processed. This drawback largely affects the accuracy of topographic measurements and characterizations of precision freeform surfaces, such as freeform optics. To address this issue, a Gaussian process machine learning-based method is presented for extrapolation of the measured surface to an extended measurement area with high accuracy prior to filtering the surface. With the extrapolated data, the edge distortion can be effectively reduced. The effectiveness of this method was evaluated using both simulated and experimental data. Successful implementation of the proposed method not only addresses the issue in surface filtering but also provides a promising solution for numerous applications involving filtering processes.

Original languageEnglish
Pages (from-to)214-224
Number of pages11
JournalMeasurement: Journal of the International Measurement Confederation
Publication statusPublished - Apr 2019


  • Edge distortion
  • Extrapolation
  • Freeform surface
  • Machine learning
  • Precision metrology
  • Surface filtering
  • Ultra-precision machining

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering

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