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UIED

dc.contributor.authorXie, Mulongen
dc.contributor.authorFeng, Sidongen
dc.contributor.authorXing, Zhenchangen
dc.contributor.authorChen, Jieshanen
dc.contributor.authorChen, Chunyangen
dc.date.accessioned2025-03-18T05:20:34Z
dc.date.available2025-03-18T05:20:34Z
dc.date.issued2020-11-08en
dc.description.abstractGraphical User Interface (GUI) elements detection is critical for many GUI automation and GUI testing tasks. Acquiring the accurate positions and classes of GUI elements is also the very first step to conduct GUI reverse engineering or perform GUI testing. In this paper, we implement a User Iterface Element Detection (UIED), a toolkit designed to provide user with a simple and easy-to-use platform to achieve accurate GUI element detection. UIED integrates multiple detection methods including old-fashioned computer vision (CV) approaches and deep learning models to handle diverse and complicated GUI images. Besides, it equips with a novel customized GUI element detection methods to produce state-of-the-art detection results. Our tool enables the user to change and edit the detection result in an interactive dashboard. Finally, it exports the detected UI elements in the GUI image to design files that can be further edited in popular UI design tools such as Sketch and Photoshop. UIED is evaluated to be capable of accurate detection and useful for downstream works. Tool URL: <a>http://uied.online</a> Github Link: <a>https://github.com/MulongXie/UIED</a>en
dc.description.statustrueen
dc.format.extent5en
dc.identifier.isbn9781450370431en
dc.identifier.otherresearchoutputwizard:a383154xPUB16238en
dc.identifier.otherScopus:85097192553en
dc.identifier.urihttps://dspace-test.anu.edu.au/handle/1885/733721177
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85097192553&partnerID=8YFLogxKen
dc.language.isoEnglishen
dc.relation.ispartofseriesESEC/FSE 2020 - Proceedings of the 28th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineeringen
dc.rightsPublisher Copyright: © 2020 ACM.en
dc.subjectComputer Visionen
dc.subjectDeep Learningen
dc.subjectObject Detectionen
dc.subjectUser Interfaceen
dc.titleUIEDen
dc.typeConference contributionen
local.bibliographicCitation.lastpage1659en
local.bibliographicCitation.startpage1655en
local.contributor.affiliationXie, Mulong; School of Computing, ANU College of Systems and Society, The Australian National Universityen
local.contributor.affiliationFeng, Sidong; Australian National Universityen
local.contributor.affiliationXing, Zhenchang; School of Computing, ANU College of Systems and Society, The Australian National Universityen
local.contributor.affiliationChen, Jieshan; Australian National Universityen
local.contributor.affiliationChen, Chunyang; Monash Universityen
local.identifier.doi10.1145/3368089.3417940en
local.identifier.pure80170b60-26fa-40ff-963c-dc16cdc40d7cen
local.type.statusPublisheden

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