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
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
109094Informations 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