RGB-D microtopography: A comprehensive dataset for surface analysis and characterization techniques.

Computer vision Confocal laser scanning microscopy Microtopography Optical metrology Surface classification Surface roughness

Journal

Data in brief
ISSN: 2352-3409
Titre abrégé: Data Brief
Pays: Netherlands
ID NLM: 101654995

Informations de publication

Date de publication:
Jun 2023
Historique:
received: 14 02 2023
revised: 20 03 2023
accepted: 21 03 2023
medline: 24 4 2023
pubmed: 24 4 2023
entrez: 24 04 2023
Statut: epublish

Résumé

The dataset presented contains microtopographies of various materials and processing methods. These microtopographies were measured using a Confocal Laser Scanning Microscope, which provides RGB-D data. This means the dataset comprises accurate height maps for each measurement and microscopic RGB images. The height maps can be used to quantify and characterize small-scale surface features such as pits and grooves, surface roughness, texture direction, and surface anisotropy. These features can significantly impact a material's properties and behavior, making them essential in many fields, such as biomaterials and tribology. Additionally, the dataset contains metadata about the specimens and the measurement conditions, such as material, surface processing method, roughness, and optical magnification. Therefore, this dataset provides an opportunity to develop and test surface classification and characterization algorithms.

Identifiants

pubmed: 37089203
doi: 10.1016/j.dib.2023.109094
pii: S2352-3409(23)00213-5
pmc: PMC10114499
doi:

Types de publication

Journal Article

Langues

eng

Pagination

109094

Informations de copyright

© 2023 The Author(s).

Déclaration de conflit d'intérêts

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Références

Materials (Basel). 2019 Dec 10;12(24):
pubmed: 31835585

Auteurs

Stefan Siemens (S)

Institute of Measurement and Automatic Control, Leibniz University Hannover, An der Universität 1, 30823 Garbsen, Germany.

Markus Kästner (M)

Institute of Measurement and Automatic Control, Leibniz University Hannover, An der Universität 1, 30823 Garbsen, Germany.

Eduard Reithmeier (E)

Institute of Measurement and Automatic Control, Leibniz University Hannover, An der Universität 1, 30823 Garbsen, Germany.

Classifications MeSH