Upright Adjustment with Graph Convolutional Networks
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jung, R. | - |
dc.contributor.author | Cho, S. | - |
dc.contributor.author | Kwon, Junseok | - |
dc.date.accessioned | 2021-05-20T07:40:46Z | - |
dc.date.available | 2021-05-20T07:40:46Z | - |
dc.date.issued | 2020-10 | - |
dc.identifier.issn | 1522-4880 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/44021 | - |
dc.description.abstract | We present a novel method for the upright adjustment of 360° images. Our network consists of two modules, which are a convolutional neural network (CNN) and a graph convolutional network (GCN). The input 360° images is processed with the CNN for visual feature extraction, and the extracted feature map is converted into a graph that finds a spherical representation of the input. We also introduce a novel loss function to address the issue of discrete probability distributions defined on the surface of a sphere. Experimental results demonstrate that our method outperforms fully connected-based methods. © 2020 IEEE. | - |
dc.format.extent | 5 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE Computer Society | - |
dc.title | Upright Adjustment with Graph Convolutional Networks | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ICIP40778.2020.9190715 | - |
dc.identifier.bibliographicCitation | Proceedings - International Conference on Image Processing, ICIP, v.2020-October, pp 1058 - 1062 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000646178501032 | - |
dc.identifier.scopusid | 2-s2.0-85098633453 | - |
dc.citation.endPage | 1062 | - |
dc.citation.startPage | 1058 | - |
dc.citation.title | Proceedings - International Conference on Image Processing, ICIP | - |
dc.citation.volume | 2020-October | - |
dc.type.docType | Conference Paper | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | Graph convolution | - |
dc.subject.keywordAuthor | Upright adjustment | - |
dc.subject.keywordPlus | Convolution | - |
dc.subject.keywordPlus | Image processing | - |
dc.subject.keywordPlus | Probability distributions | - |
dc.subject.keywordPlus | Convolutional networks | - |
dc.subject.keywordPlus | Discrete probability distribution | - |
dc.subject.keywordPlus | Feature map | - |
dc.subject.keywordPlus | Loss functions | - |
dc.subject.keywordPlus | Spherical representation | - |
dc.subject.keywordPlus | Visual feature extraction | - |
dc.subject.keywordPlus | Convolutional neural networks | - |
dc.relation.journalResearchArea | Imaging Science & Photographic Technology | - |
dc.relation.journalWebOfScienceCategory | Imaging Science & Photographic Technology | - |
dc.description.journalRegisteredClass | scopus | - |
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