Crowdsourcing quality control for Dark Energy Survey images.
Peter MelchiorErin SheldonAlex Drlica-WagnerEli S. RykoffTimothy M. C. AbbottFilipe B. AbdallaSahar AllamAurélien Benoit-LévyDavid D. BrooksElizabeth Buckley-GeerAurelio Carnero RosellMatias Carrasco KindJorge CarreteroMartín CrocceChris D'AndreaLuiz Nicolaci da CostaShantanu DesaiPeter DoelAugust E. EvrardDavid A. FinleyBrenna L. FlaugherJoshua A. FriemanEnrique GaztañagaDavid W. GerdesDaniel GruenRichard A. GruendlKlaus HonscheidDavid J. JamesMichael J. JarvisKyler W. KuehnTing S. LiMarcio A. G. MaiaMarisa C. MarchJennifer L. MarshallBrian NordRicardo OgandoAndreas Alejandro PlazasAnita K. RömerEusebio SánchezVictor E. ScarpineIgnacio Sevilla-NoarbeRobert C. SmithMarcelle Soares-SantosEric SuchytaMollye E. C. SwansonGregory G. TarléVinu VikramAlistair R. WalkerWilliam C. WesterYuanyuan ZhangPublished in: Astron. Comput. (2016)
Keyphrases
- quality control
- machine vision
- image data
- quality assurance
- three dimensional
- image analysis
- image registration
- image database
- image classification
- ground truth
- input image
- test images
- image collections
- edge detection
- image features
- object recognition
- segmentation algorithm
- product quality
- lighting conditions
- image set
- real time
- automated visual inspection
- software engineering
- image retrieval
- cooperative
- computer vision