Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/28646
DC FieldValueLanguage
dc.contributor.authorTheodosiou, Zenonas-
dc.contributor.authorThoma, Marios-
dc.contributor.authorPartaourides, Harris-
dc.contributor.authorLanitis, Andreas-
dc.date.accessioned2023-03-20T20:07:34Z-
dc.date.available2023-03-20T20:07:34Z-
dc.date.issued2022-09-
dc.identifier.citationAlgorithms, 2022, vol. 15, no. 9, articl. no. 305en_US
dc.identifier.issn19994893-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/28646-
dc.description.abstractThe provision of information encourages people to visit cultural sites more often. Exploiting the great potential of using smartphone cameras and egocentric vision, we describe the development of a robust artwork recognition algorithm to assist users when visiting an art space. The algorithm recognizes artworks under any physical museum conditions, as well as camera point of views, making it suitable for different use scenarios towards an enhanced visiting experience. The algorithm was developed following a multiphase approach, including requirements gathering, experimentation in a virtual environment, development of the algorithm in real environment conditions, implementation of a demonstration smartphone app for artwork recognition and provision of assistive information, and its evaluation. During the algorithm development process, a convolutional neural network (CNN) model was trained for automatic artwork recognition using data collected in an art gallery, followed by extensive evaluations related to the parameters that may affect recognition accuracy, while the optimized algorithm was also evaluated through a dedicated app by a group of volunteers with promising results. The overall algorithm design and evaluation adopted for this work can also be applied in numerous applications, especially in cases where the algorithm performance under varying conditions and end-user satisfaction are critical factors.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofAlgorithmsen_US
dc.rights© by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.en_US
dc.subjectEgocentric visionen_US
dc.subjectArtwork recognitionen_US
dc.subjectVisiting experienceen_US
dc.subjectSmartphone appen_US
dc.subjectDeep learningen_US
dc.titleA Systematic Approach for Developing a Robust Artwork Recognition Framework Using Smartphone Camerasen_US
dc.typeArticleen_US
dc.collaborationCYENS - Centre of Excellenceen_US
dc.collaborationCyprus University of Technologyen_US
dc.subject.categoryElectrical Engineering - Electronic Engineering - Information Engineeringen_US
dc.journalsOpen Accessen_US
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.3390/a15090305en_US
dc.identifier.scopus2-s2.0-85138482627-
dc.identifier.urlhttps://doi.org/10.3390/a15090305-
dc.relation.issue9en_US
dc.relation.volume15en_US
cut.common.academicyear2022-2023en_US
dc.identifier.external118024673-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.cerifentitytypePublications-
item.openairetypearticle-
crisitem.journal.journalissn1999-4893-
crisitem.journal.publisherMDPI-
crisitem.author.deptDepartment of Communication and Internet Studies-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.deptDepartment of Multimedia and Graphic Arts-
crisitem.author.facultyFaculty of Communication and Media Studies-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.facultyFaculty of Fine and Applied Arts-
crisitem.author.orcid0000-0003-3168-2350-
crisitem.author.orcid0000-0002-8555-260X-
crisitem.author.orcid0000-0001-6841-8065-
crisitem.author.parentorgFaculty of Communication and Media Studies-
crisitem.author.parentorgFaculty of Engineering and Technology-
crisitem.author.parentorgFaculty of Fine and Applied Arts-
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