Gaussian process based bayesian inference system for intelligent surface measurement

Ming Jun Ren, Chi Fai Cheung, Gao Bo Xiao (Corresponding Author)

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)


This paper presents a Gaussian process based Bayesian inference system for the realization of intelligent surface measurement on multi-sensor instruments. The system considers the surface measurement as a time series data collection process, and the Gaussian process is used as mathematical foundation to establish an inferring plausible model to aid the measurement process via multi-feature classification and multi-dataset regression. Multi-feature classification extracts and classifies the geometric features of the measured surfaces at different scales to design an appropriate composite covariance kernel and corresponding initial sampling strategy. Multi-dataset regression takes the designed covariance kernel as input to fuse the multi-sensor measured datasets with Gaussian process model, which is further used to adaptively refine the initial sampling strategy by taking the credibility of the fused model as the critical sampling criteria. Hence, intelligent sampling can be realized with consecutive learning process with full Bayesian treatment. The statistical nature of the Gaussian process model combined with various powerful covariance kernel functions offer the system great flexibility for different kinds of complex surfaces.

Original languageEnglish
Article number4069
JournalSensors (Switzerland)
Issue number11
Publication statusPublished - 21 Nov 2018


  • Data fusion
  • Gaussian process
  • Multi-sensor measurement
  • Surface measurement
  • Surface modelling

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering


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