2011
DOI: 10.1080/0740817x.2010.521804
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Bayesian hierarchical model for combining misaligned two-resolution metrology data

Abstract: This article presents a Bayesian hierarchical model to combine misaligned two-resolution metrology data for inspecting the geometric quality of manufactured parts. High-resolution data points are scarce and scatter over the surface being measured, while low-resolution data are pervasive but less accurate and less precise. Combining the two datasets should produce better predictions than using a single dataset. One challenge in combining them is the misalignment existing between data from different resolutions.… Show more

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Cited by 36 publications
(34 citation statements)
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“…Thus, fusion of x, y and z data can be reduced down to fusion of z data only. Typical point cloud fusion algorithms include hierarchical Gaussian process fusion and its derivatives [59][60][61][62][63]. These Gaussian process-based fusion methods approximate the residuals between two reliability-differentiated datasets using Gaussian process models.…”
Section: ) Point Cloud Fusionmentioning
confidence: 99%
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“…Thus, fusion of x, y and z data can be reduced down to fusion of z data only. Typical point cloud fusion algorithms include hierarchical Gaussian process fusion and its derivatives [59][60][61][62][63]. These Gaussian process-based fusion methods approximate the residuals between two reliability-differentiated datasets using Gaussian process models.…”
Section: ) Point Cloud Fusionmentioning
confidence: 99%
“…Denoising as a pre-process is optional because many registration [48] and fusion [62] processes are insensitive to noise. In some fusion methods, denoising is included in the fusion process, such as in Gaussian process fusion [63].…”
Section: ) Denoisingmentioning
confidence: 99%
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“…A different class of hierarchical models can be defined, that take into account the potential variability or uncertainty of the response, as in (Xia et al, 2011), where a hierarchical Bayesian model is implemented. These models have been more recently and therefore less frequently applied and they are suited not just for merging the results of Hi-Fi and Lo-Fi simulations, but for the fusion of computer experiments and physical experiments.…”
Section: Introductionmentioning
confidence: 99%