Performance evaluation of multi-scale data fusion methods for surface metrology domain

With the rapid evolution of new engineered surfaces, there is a strong need for developing tools to measure and characterize these surfaces at different scales. In order to obtain all meaningful details of the surface at various required scales, data fusion can be performed on data obtained from a c...

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Bibliographic Details
Published inJournal of manufacturing systems Vol. 32; no. 4; pp. 514 - 522
Main Authors Ramasamy, Suresh K., Raja, Jayaraman
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.10.2013
Subjects
Engineered surfaces
Fusion metrics
Multi-sensor data fusion
Surface metrology
Fusion metrics
Engineered surfaces
Surface metrology
Multi-sensor data fusion
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Summary:With the rapid evolution of new engineered surfaces, there is a strong need for developing tools to measure and characterize these surfaces at different scales. In order to obtain all meaningful details of the surface at various required scales, data fusion can be performed on data obtained from a combination of instruments or technologies. In order to evaluate the fusion methods, typically, well-recognized images like ‘Lena’ are used. But surface metrology datasets are distinctly different from those images, as all the data points are in focus, compared to typical images with a subject in focus and background with various levels of out-of-focus. So, a performance study was conducted on a wide range of surface samples and it was shown that Regional Edge Intensity (REI) is the preferred fusion method for surface metrology datasets, and Regional Energy (RE) is the second preferred method, when single-scale performance metrics are considered.
ISSN:0278-6125
1878-6642
DOI:10.1016/j.jmsy.2013.05.013