Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/140529
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dc.contributor.authorGe, Liuhaoen_US
dc.contributor.authorLiang, Huien_US
dc.contributor.authorYuan, Junsongen_US
dc.contributor.authorThalmann, Danielen_US
dc.date.accessioned2020-05-30T06:11:41Z-
dc.date.available2020-05-30T06:11:41Z-
dc.date.issued2018-
dc.identifier.citationGe, L., Liang, H., Yuan, J., & Thalmann, D. (2018). Robust 3D hand pose estimation from single depth images using multi-view CNNs. IEEE Transactions on Image Processing, 27(9), 4422-4436. doi:10.1109/TIP.2018.2834824en_US
dc.identifier.issn1057-7149en_US
dc.identifier.urihttps://hdl.handle.net/10356/140529-
dc.description.abstractArticulated hand pose estimation is one of core technologies in human-computer interaction. Despite the recent progress, most existing methods still cannot achieve satisfactory performance, partly due to the difficulty of the embedded high-dimensional nonlinear regression problem. Most existing data-driven methods directly regress 3D hand pose from 2D depth image, which cannot fully utilize the depth information. In this paper, we propose a novel multi-view convolutional neural network (CNN)-based approach for 3D hand pose estimation. To better exploit 3D information in the depth image, we project the point cloud generated from the query depth image onto multiple views of two projection settings and integrate them for more robust estimation. Multi-view CNNs are trained to learn the mapping from projected images to heat-maps, which reflect probability distributions of joints on each view. These multi-view heat-maps are then fused to estimate the optimal 3D hand pose with learned pose priors, and the unreliable information in multi-view heat-maps is suppressed using a view selection method. Experimental results show that the proposed method is superior to the state-of-the-art methods on two challenging data sets. Furthermore, a cross-data set experiment also validates that our proposed approach has good generalization ability.en_US
dc.description.sponsorshipNRF (Natl Research Foundation, S’pore)en_US
dc.description.sponsorshipMOE (Min. of Education, S’pore)en_US
dc.language.isoenen_US
dc.relationMOE2015-T2-2-114en_US
dc.relation.ispartofIEEE Transactions on Image Processingen_US
dc.rights© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TIP.2018.2834824en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleRobust 3D hand pose estimation from single depth images using multi-view CNNsen_US
dc.typeJournal Articleen
dc.contributor.schoolInterdisciplinary Graduate School (IGS)en_US
dc.contributor.researchInstitute for Media Innovation (IMI)en_US
dc.identifier.doi10.1109/TIP.2018.2834824-
dc.description.versionAccepted versionen_US
dc.identifier.issue9en_US
dc.identifier.volume27en_US
dc.identifier.spage4422en_US
dc.identifier.epage4436en_US
dc.subject.keywordsThree-dimensional Displaysen_US
dc.subject.keywordsHeating Systemsen_US
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item.grantfulltextopen-
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