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Titlebook: Computer Vision – ACCV 2022; 16th Asian Conferenc Lei Wang,Juergen Gall,Rama Chellappa Conference proceedings 2023 The Editor(s) (if applic

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發(fā)表于 2025-3-21 17:15:35 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Computer Vision – ACCV 2022
副標(biāo)題16th Asian Conferenc
編輯Lei Wang,Juergen Gall,Rama Chellappa
視頻videohttp://file.papertrans.cn/235/234134/234134.mp4
叢書(shū)名稱Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Computer Vision – ACCV 2022; 16th Asian Conferenc Lei Wang,Juergen Gall,Rama Chellappa Conference proceedings 2023 The Editor(s) (if applic
描述.The 7-volume set of LNCS 13841-13847 constitutes the proceedings of the 16th Asian Conference on Computer Vision, ACCV 2022, held in Macao, China, December 2022...The total of 277 contributions included in the proceedings set was carefully reviewed and selected from 836 submissions during two rounds of reviewing and improvement. The papers focus on the following topics:..Part I: 3D computer vision; optimization methods;.Part II: applications of computer vision, vision for X; computational photography, sensing, and display;..Part III: low-level vision, image processing; ..Part IV: face and gesture; pose and action; video analysis and event recognition; vision and language; biometrics;..Part V: recognition: feature detection, indexing, matching, and shape representation; datasets and performance analysis;.Part VI: biomedical image analysis; deep learning for computer vision; ..Part VII: generative models for computer vision; segmentation and grouping; motion and tracking; document image analysis; big data, large scale methods. .
出版日期Conference proceedings 2023
關(guān)鍵詞artificial intelligence; color image processing; color images; computer networks; education; engineering;
版次1
doihttps://doi.org/10.1007/978-3-031-26313-2
isbn_softcover978-3-031-26312-5
isbn_ebook978-3-031-26313-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Jeyakrishna Velauthapillai,Johannes Flo?-HD images..The extensive experiments on three large-scale image desnowing datasets demonstrate that our method surpasses all state-of-the-art approaches by a large margin both quantitatively and qualitatively, boosting the PSNR metric from 31.76 dB to 34.10 dB on the CSD test dataset and from 28.29
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Jens Freche,Milan den Heijer,Bastian Wormuthation performance caused by training on synthetic datasets. Quantitative and qualitative experiments show that the proposed method significantly outperforms state-of-the-art methods on real-world cloud images. The source code and dataset are available at ..
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From Textualism to Hypertextualism’s density and uneven distribution. Based on the uncertainty map, our feedback network refines our defogged output iteratively. Moreover, to handle the intractability of estimating the atmospheric light colors, we exploit the grayscale version of our input image, since it is less affected by varying
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