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Titlebook: Computer Vision – ACCV 2020; 15th Asian Conferenc Hiroshi Ishikawa,Cheng-Lin Liu,Jianbo Shi Conference proceedings 2021 Springer Nature Swi

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书目名称Computer Vision – ACCV 2020
副标题15th Asian Conferenc
编辑Hiroshi Ishikawa,Cheng-Lin Liu,Jianbo Shi
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Computer Vision – ACCV 2020; 15th Asian Conferenc Hiroshi Ishikawa,Cheng-Lin Liu,Jianbo Shi Conference proceedings 2021 Springer Nature Swi
描述The six volume set of LNCS 12622-12627 constitutes the proceedings of the 15th Asian Conference on Computer Vision, ACCV 2020, held in Kyoto, Japan, in November/ December 2020.*.The total of 254 contributions was carefully reviewed and selected from 768 submissions during two rounds of reviewing and improvement. The papers focus on the following topics:..Part I: 3D computer vision; segmentation and grouping..Part II: low-level vision, image processing; motion and tracking..Part III: recognition and detection; optimization, statistical methods, and learning; robot vision.Part IV: deep learning for computer vision, generative models for computer vision..Part V: face, pose, action, and gesture; video analysis and event recognition; biomedical image analysis..Part VI: applications of computer vision; vision for X; datasets and performance analysis..*The conference was held virtually..
出版日期Conference proceedings 2021
关键词3d objects; artificial intelligence; biomedical image analysis; computer networks; computer vision; image
版次1
doihttps://doi.org/10.1007/978-3-030-69525-5
isbn_softcover978-3-030-69524-8
isbn_ebook978-3-030-69525-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

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书目名称Computer Vision – ACCV 2020读者反馈学科排名




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Multi-view Consistency Loss for Improved Single-Image 3D Reconstruction of Clothed Peopleed to allows transfer to reconstruction from real images of people. Comprehensive comparative performance evaluation on both synthetic and real images of people demonstrates that the proposed method significantly outperforms the previous state-of-the-art learning-based single image 3D human shape es
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AFN: Attentional Feedback Network Based 3D Terrain Super-Resolutionroduce the terrain realistically. We compare the proposed architecture with existing state-of-the-art DEM super-resolution methods and show that the proposed architecture outperforms enhancing the resolution of input LRDEM accurately and in a realistic manner.
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