{"@context":{"@vocab":"https://cir.nii.ac.jp/schema/1.0/","rdfs":"http://www.w3.org/2000/01/rdf-schema#","dc":"http://purl.org/dc/elements/1.1/","dcterms":"http://purl.org/dc/terms/","foaf":"http://xmlns.com/foaf/0.1/","prism":"http://prismstandard.org/namespaces/basic/2.0/","cinii":"http://ci.nii.ac.jp/ns/1.0/","datacite":"https://schema.datacite.org/meta/kernel-4/","ndl":"http://ndl.go.jp/dcndl/terms/","jpcoar":"https://github.com/JPCOAR/schema/blob/master/2.0/"},"@id":"https://cir.nii.ac.jp/crid/1361699993837820672.json","@type":"Article","productIdentifier":[{"identifier":{"@type":"DOI","@value":"10.5194/angeo-22-1103-2004"}},{"identifier":{"@type":"URI","@value":"https://angeo.copernicus.org/articles/22/1103/2004/angeo-22-1103-2004.pdf"}}],"dc:title":[{"@value":"Diurnal auroral occurrence statistics obtained via machine vision"}],"description":[{"type":"abstract","notation":[{"@value":"<jats:p>Abstract. Modern ground-based digital auroral All-Sky Imager (ASI) networks capture millions of images annually. Machine vision techniques are widely utilised in the retrieval of images from large data bases. Clearly, they can play an important scientific role in dealing with data from auroral ASI networks, facilitating both efficient searches and statistical studies. Furthermore, the development of automated techniques for identifying specific types of aurora opens up the potential of ASI control software that would change instrument operation in response to evolving geophysical conditions. In this paper, we describe machine vision techniques that we have developed for use on large auroral image data sets. We present the results of application of these techniques to a 350000 image subset of the CANOPUS Gillam ASI in the years 1993–1998. In particular, we obtain occurrence statistics for auroral arcs, patches, and Omega-bands. These results agree with those of previous manual auroral surveys.Key words. Ionosphere (Instruments and techniques) General (new fields)\n                    </jats:p>"}]}],"creator":[{"@id":"https://cir.nii.ac.jp/crid/1380580229812901251","@type":"Researcher","foaf:name":[{"@value":"M. T. Syrjäsuo"}]},{"@id":"https://cir.nii.ac.jp/crid/1381699993837820673","@type":"Researcher","foaf:name":[{"@value":"E. F. Donovan"}]}],"publication":{"publicationIdentifier":[{"@type":"EISSN","@value":"14320576"}],"prism:publicationName":[{"@value":"Annales Geophysicae"}],"dc:publisher":[{"@value":"Copernicus GmbH"}],"prism:publicationDate":"2004-04-02","prism:volume":"22","prism:number":"4","prism:startingPage":"1103","prism:endingPage":"1113"},"reviewed":"false","dc:rights":["https://creativecommons.org/licenses/by/3.0/"],"url":[{"@id":"https://angeo.copernicus.org/articles/22/1103/2004/angeo-22-1103-2004.pdf"}],"createdAt":"2010-06-15","modifiedAt":"2021-01-28","relatedProduct":[{"@id":"https://cir.nii.ac.jp/crid/1360567179757506944","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Omega band pulsating auroras observed onboard THEMIS spacecraft and on the ground"}]},{"@id":"https://cir.nii.ac.jp/crid/1360580229812901376","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"An automated auroral detection system using deep learning: real-time operation in Tromsø, Norway"}]},{"@id":"https://cir.nii.ac.jp/crid/2050588892073514752","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Morphologies of omega band auroras"}]}],"dataSourceIdentifier":[{"@type":"CROSSREF","@value":"10.5194/angeo-22-1103-2004"},{"@type":"CROSSREF","@value":"10.1186/s40623-017-0688-1_references_DOI_4ocXJOh5jE3dPNMpwTn9CjTpI2o"},{"@type":"CROSSREF","@value":"10.1002/2015ja021382_references_DOI_4ocXJOh5jE3dPNMpwTn9CjTpI2o"},{"@type":"CROSSREF","@value":"10.1038/s41598-022-11686-8_references_DOI_4ocXJOh5jE3dPNMpwTn9CjTpI2o"}]}