Please use this identifier to cite or link to this item: https://repository.cihe.edu.hk/jspui/handle/cihe/438
DC FieldValueLanguage
dc.contributor.authorLiu, Xuetingen_US
dc.contributor.authorLi, Chengze-
dc.contributor.otherWong, T.-T.-
dc.date.accessioned2021-03-27T08:32:41Z-
dc.date.available2021-03-27T08:32:41Z-
dc.date.issued2017-
dc.identifier.urihttps://repository.cihe.edu.hk/jspui/handle/cihe/438-
dc.description.abstractExtraction of structural lines from pattern-rich manga is a crucial step for migrating legacy manga to digital domain. Unfortunately, it is very challenging to distinguish structural lines from arbitrary, highly-structured, and black-and-white screen patterns. In this paper, we present a novel data-driven approach to identify structural lines out of pattern-rich manga, with no assumption on the patterns. The method is based on convolutional neural networks. To suit our purpose, we propose a deep network model to handle the large variety of screen patterns and raise output accuracy. We also develop an efficient and effective way to generate a rich set of training data pairs. Our method suppresses arbitrary screen patterns no matter whether these patterns are regular, irregular, tone-varying, or even pictorial, and regardless of their scales. It outputs clear and smooth structural lines even if these lines are contaminated by and immersed in complex patterns. We have evaluated our method on a large number of mangas of various drawing styles. Our method substantially outperforms state-of-the-art methods in terms of visual quality. We also demonstrate its potential in various manga applications, including manga colorization, manga retargeting, and 2.5D manga generation.en_US
dc.language.isoenen_US
dc.publisherAssociation for Computing Machineryen_US
dc.relation.ispartofACM Transactions on Graphicsen_US
dc.titleDeep extraction of manga structural linesen_US
dc.typejournal articleen_US
dc.identifier.doi10.1145/3072959.3073675-
dc.contributor.affiliationSchool of Computing and Information Sciencesen_US
dc.relation.issn1557-7368en_US
dc.description.volume36en_US
dc.description.issue4en_US
dc.cihe.affiliatedNo-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.fulltextWith Fulltext-
item.cerifentitytypePublications-
item.openairetypejournal article-
item.grantfulltextopen-
crisitem.author.deptSchool of Computing and Information Sciences-
crisitem.author.deptSchool of Computing and Information Sciences-
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