Mathematical approach to the validation of surface texture form removal software Article Swipe
Luke Todhunter
,
Richard Leach
,
François Blateyron
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.4063715
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.4063715
A new approach to the validation of surface texture form removal methods is introduced. A linear algebra technique is presented that obtains total least squares (TLS) model fits for a continuous mathematical surface definition. This model is applicable to both profile and areal form removal, and can be used for a range of form removal models including polynomial and spherical fits. The continuous TLS method enables the creation of mathematically traceable reference pairs suitable for the assessment of form removal algorithms in surface texture analysis software. Multiple example reference pairs are presented and used to assess the performance of four tested surface texture analysis software packages. The results of each software are compared against the mathematical reference, highlighting their strengths and weaknesses.
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Metadata
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All OpenAlex metadata
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- DOI
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https://doi.org/10.5281/zenodo.4063715Digital Object Identifier
- Title
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Mathematical approach to the validation of surface texture form removal softwareWork title
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datasetOpenAlex work type
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enPrimary language
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2020Year of publication
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2020-10-02Full publication date if available
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Luke Todhunter, Richard Leach, François BlateyronList of authors in order
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https://doi.org/10.5281/zenodo.4063715Publisher landing page
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://doi.org/10.5281/zenodo.4063715Direct OA link when available
- Concepts
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Texture (cosmology), Software, Surface (topology), Computer science, Artificial intelligence, Mathematics, Geometry, Image (mathematics), Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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