skip to main content
Language:
Search Limited to: Search Limited to: Resource type Show Results with: Show Results with: Search type Index

The Cube++ Illumination Estimation Dataset

IEEE access, 2020, Vol.8, p.227511-227527 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2020.3045066 ;CODEN: IAECCG

Full text available

Citations Cited by
  • Title:
    The Cube++ Illumination Estimation Dataset
  • Author: Ershov, Egor ; Savchik, Alexey ; Semenkov, Illya ; Banic, Nikola ; Belokopytov, Alexander ; Senshina, Daria ; Koscevic, Karlo ; Subasic, Marko ; Loncaric, Sven
  • Subjects: Calibration ; Color ; Color constancy ; dataset ; Datasets ; Digital cameras ; Digital imaging ; Estimation ; Illumination ; illumination estimation ; Image color analysis ; Image processing ; Image quality ; Light sources ; Lighting ; mixed illumination ; multiple illumination ; Publishing ; Semantics ; Target recognition ; white balancing
  • Is Part Of: IEEE access, 2020, Vol.8, p.227511-227527
  • Description: Computational color constancy has the important task of reducing the influence of the scene illumination on the object colors. As such, it is an essential part of the image processing pipelines of most digital cameras. One of the important parts of the computational color constancy is illumination estimation, i.e. estimating the illumination color. When an illumination estimation method is proposed, its accuracy is usually reported by providing the values of error metrics obtained on the images of publicly available datasets. However, over time it has been shown that many of these datasets have problems such as too few images, inappropriate image quality, lack of scene diversity, absence of version tracking, violation of various assumptions, GDPR regulation violation, lack of additional shooting procedure info, etc. In this paper a new illumination estimation dataset is proposed that aims to alleviate many of the mentioned problems and to help the illumination estimation research. It consists of 4890 images with known illumination colors as well as with additional semantic data that can further make the learning process more accurate. Due to the usage of the SpyderCube color target, for every image there are two ground-truth illumination records covering different directions. Because of that, the dataset can be used for training and testing of methods that perform single or two-illuminant estimation. This makes it superior to many similar existing datasets. The datasets, it's smaller version SimpleCube++, and the accompanying code are available at https://github.com/Visillect/CubePlusPlus/ .
  • Publisher: Piscataway: IEEE
  • Language: English
  • Identifier: ISSN: 2169-3536
    EISSN: 2169-3536
    DOI: 10.1109/ACCESS.2020.3045066
    CODEN: IAECCG
  • Source: IEEE Open Access Journals
    DOAJ Directory of Open Access Journals
Back to results list
INSPIRE LIBRARY - TON DUC THANG UNIVERSITY (84-028) 37 755 057 Feedback
19 Nguyen Huu Tho St. Dist.7, HCM thuvien@tdtu.edu.vn

Copyright © 2017 INSPIRE LIBRARY - TON DUC THANG UNIVERSITY

Searching Remote Databases, Please Wait