Calibration images under different lighting conditions - static for feature localisation - Open Research Data - Bridge of Knowledge

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Calibration images under different lighting conditions - static for feature localisation

Description

Dataset description: Calibration images under different lighting conditions - static mode for feature detection

Calibration photos under different lighting conditions. The set includes a geometric calibration matrix, a color standard, a spectrometer and a lamp with a variable lighting spectrum. 10 static images were taken in each of the 12 sessions. The light spectrum was measured in each session. The results of the light spectrum measurements can be found in each folder respectively. Calibration results from each session for the same camera are in the same folder named the sesion number.  

Object: Geometric calibration matrix, color calibration matrix
Location: Laboratory
Camera type: Sony Alpha 6000 (details in metadata)
Metadata data: yes
Model type: Perspective
Image dimensions: 6000x3376pixels
Sensor size: APSC
Number of photos: 120

Aperature: F/2.8
File format: RAW ARW (Sony)
Focus: Manual 
Shuter speed: 1/40
ISO 320

Session/Lamp Settings:
1 Tungusten
2 Modeling Lamp
3 White Halogen
4 Horizon Daylight
5 Daylight
6 R
7 G
8 B
9 RGB
10 GB
11 RB
12 RG

Spectrometer type: UPRtec MK350D

Results: Spectro data in xls and TXT file

Specrum test reports: yes / 3 per session

Spectro data description: eg. Spektro2-001_p1

001- sesion number 
p1- report type

Callibration results: No

Laboratory set diagram

Ilustracja publikacji

Example photo

Ilustracja publikacji

Example spectum

Ilustracja publikacji

Dataset file

CalibrationImagesStatic.zip
2.6 GB, S3 ETag 107a92525d73640229435e005c01a079-6, downloads: 58
The file hash is calculated from the formula
hexmd5(md5(part1)+md5(part2)+...)-{parts_count} where a single part of the file is 512 MB in size.

Example script for calculation:
https://github.com/antespi/s3md5
download file CalibrationImagesStatic.zip

File details

License:
Creative Commons: by 4.0 open in new tab
CC BY
Attribution

Details

Year of publication:
2024
Verification date:
2024-06-17
Creation date:
2023
Dataset language:
English
Fields of science:
  • civil engineering, geodesy and transport (Engineering and Technology)
DOI:
DOI ID 10.34808/sb8g-tm87 open in new tab
Series:
Verified by:
Gdańsk University of Technology

Keywords

References

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