Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 109094 |
| Fachzeitschrift | Data in Brief |
| Jahrgang | 48 |
| Frühes Online-Datum | 28 März 2023 |
| Publikationsstatus | Veröffentlicht - Juni 2023 |
Abstract
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.
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in: Data in Brief, Jahrgang 48, 109094, 06.2023.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - RGB-D microtopography
T2 - A comprehensive dataset for surface analysis and characterization techniques
AU - Siemens, Stefan
AU - Kästner, Markus
AU - Reithmeier, Eduard
N1 - Funding Information: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
PY - 2023/6
Y1 - 2023/6
N2 - 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.
AB - 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.
KW - Computer vision
KW - Confocal laser scanning microscopy
KW - Microtopography
KW - Optical metrology
KW - Surface classification
KW - Surface roughness
UR - http://www.scopus.com/inward/record.url?scp=85151500727&partnerID=8YFLogxK
U2 - 10.1016/j.dib.2023.109094
DO - 10.1016/j.dib.2023.109094
M3 - Article
AN - SCOPUS:85151500727
VL - 48
JO - Data in Brief
JF - Data in Brief
M1 - 109094
ER -