Application of Clustering Filter for Noise and Outlier Suppression in Optical Measurement of Structured Surfaces

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Abstract

In comparison to tactile sensors, optical techniques can provide a fast, non-destructive profile/areal surface measurement solution. Nonetheless, high measurement noise, unmeasured points and outliers, are often observed in optical measurement, particularly for structured surfaces. To alleviate their detrimental impacts on the characterization of surface topography as well as the examination of micro/nanoscale geometries, a post-processing filtering technique, i.e. the clustering filter, which is essentially an iterative process to find the aggregation center of a cluster of points, is implemented. The clustering filter is particularly useful for noises and outlier suppression for optical measurement of structured surfaces due to its edge-preserving capability. Five surface samples with structured features are measured by an in-house developed dispersive interferometer and a commercial white light interferometer, thereafter the measured surface data is filtered by the clustering filter. Both noise and outliers are suppressed, which not only facilitates the visualization and characterization of surface topography, but also enables the accurate evaluation of local functional geometries.
Original languageEnglish
JournalIEEE Transactions on Instrumentation and Measurement
Publication statusAccepted/In press - 4 Jan 2020

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optical measurement
retarding
filters
Surface topography
Interferometers
Geometry
Surface measurement
Optical sensors
topography
interferometers
Agglomeration
Visualization
noise measurement
geometry
preserving
Processing
examination
evaluation
profiles

Cite this

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title = "Application of Clustering Filter for Noise and Outlier Suppression in Optical Measurement of Structured Surfaces",
abstract = "In comparison to tactile sensors, optical techniques can provide a fast, non-destructive profile/areal surface measurement solution. Nonetheless, high measurement noise, unmeasured points and outliers, are often observed in optical measurement, particularly for structured surfaces. To alleviate their detrimental impacts on the characterization of surface topography as well as the examination of micro/nanoscale geometries, a post-processing filtering technique, i.e. the clustering filter, which is essentially an iterative process to find the aggregation center of a cluster of points, is implemented. The clustering filter is particularly useful for noises and outlier suppression for optical measurement of structured surfaces due to its edge-preserving capability. Five surface samples with structured features are measured by an in-house developed dispersive interferometer and a commercial white light interferometer, thereafter the measured surface data is filtered by the clustering filter. Both noise and outliers are suppressed, which not only facilitates the visualization and characterization of surface topography, but also enables the accurate evaluation of local functional geometries.",
keywords = "Surface measurement, optical metrology, denoising algorithm, clustering filter",
author = "Shan Lou and Dawei Tang and Wenhan Zeng and Tao Zhang and Feng Gao and Hussam Muhamedsalih and Jane Jiang and Paul Scott",
year = "2020",
month = "1",
day = "4",
language = "English",
journal = "IEEE Transactions on Instrumentation and Measurement",
issn = "0018-9456",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

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TY - JOUR

T1 - Application of Clustering Filter for Noise and Outlier Suppression in Optical Measurement of Structured Surfaces

AU - Lou, Shan

AU - Tang, Dawei

AU - Zeng, Wenhan

AU - Zhang, Tao

AU - Gao, Feng

AU - Muhamedsalih, Hussam

AU - Jiang, Jane

AU - Scott, Paul

PY - 2020/1/4

Y1 - 2020/1/4

N2 - In comparison to tactile sensors, optical techniques can provide a fast, non-destructive profile/areal surface measurement solution. Nonetheless, high measurement noise, unmeasured points and outliers, are often observed in optical measurement, particularly for structured surfaces. To alleviate their detrimental impacts on the characterization of surface topography as well as the examination of micro/nanoscale geometries, a post-processing filtering technique, i.e. the clustering filter, which is essentially an iterative process to find the aggregation center of a cluster of points, is implemented. The clustering filter is particularly useful for noises and outlier suppression for optical measurement of structured surfaces due to its edge-preserving capability. Five surface samples with structured features are measured by an in-house developed dispersive interferometer and a commercial white light interferometer, thereafter the measured surface data is filtered by the clustering filter. Both noise and outliers are suppressed, which not only facilitates the visualization and characterization of surface topography, but also enables the accurate evaluation of local functional geometries.

AB - In comparison to tactile sensors, optical techniques can provide a fast, non-destructive profile/areal surface measurement solution. Nonetheless, high measurement noise, unmeasured points and outliers, are often observed in optical measurement, particularly for structured surfaces. To alleviate their detrimental impacts on the characterization of surface topography as well as the examination of micro/nanoscale geometries, a post-processing filtering technique, i.e. the clustering filter, which is essentially an iterative process to find the aggregation center of a cluster of points, is implemented. The clustering filter is particularly useful for noises and outlier suppression for optical measurement of structured surfaces due to its edge-preserving capability. Five surface samples with structured features are measured by an in-house developed dispersive interferometer and a commercial white light interferometer, thereafter the measured surface data is filtered by the clustering filter. Both noise and outliers are suppressed, which not only facilitates the visualization and characterization of surface topography, but also enables the accurate evaluation of local functional geometries.

KW - Surface measurement

KW - optical metrology

KW - denoising algorithm

KW - clustering filter

M3 - Article

JO - IEEE Transactions on Instrumentation and Measurement

JF - IEEE Transactions on Instrumentation and Measurement

SN - 0018-9456

ER -