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Titlebook: Computer Vision and Image Processing; 7th International Co Deep Gupta,Kishor Bhurchandi,Sanjeev Kumar Conference proceedings 2023 The Edito

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書目名稱Computer Vision and Image Processing
副標題7th International Co
編輯Deep Gupta,Kishor Bhurchandi,Sanjeev Kumar
視頻videohttp://file.papertrans.cn/235/234062/234062.mp4
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: Computer Vision and Image Processing; 7th International Co Deep Gupta,Kishor Bhurchandi,Sanjeev Kumar Conference proceedings 2023 The Edito
描述.This two volume set (CCIS 1776-1777) constitutes the refereed proceedings of the 7th International Conference on Computer Vision and Image Processing, CVIP 2022, held in Nagpur, India, November 4–6, 2022...The 110 full papers and 11 short papers?were carefully reviewed and selected from 307 submissions. Out of 121 papers, 109 papers are included in this book. The topical scope of the two-volume set focuses on Medical?Image? Analysis,? Image/? Video? Processing? for? Autonomous? Vehicles,? Activity Detection/? Recognition,? Human? Computer? Interaction,? Segmentation? and? Shape Representation,? Motion? and? Tracking,? Image/? Video? Scene? Understanding,? Image/Video? Retrieval,? Remote? Sensing,? Hyperspectral? Image? Processing,? Face,? Iris,?Emotion, Sign Language and Gesture Recognition, etc..
出版日期Conference proceedings 2023
關鍵詞Computer Science; Informatics; Conference Proceedings; Research; Applications
版次1
doihttps://doi.org/10.1007/978-3-031-31407-0
isbn_softcover978-3-031-31406-3
isbn_ebook978-3-031-31407-0Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
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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Advances in Cryptology – CRYPTO 2016orrectly. The second dataset consists of masked faces and faces without masks. To validate the generalization capability of the proposed model, the trained model is tested on two new standard datasets. In addition to that, the testing is done on a dataset created by ourselves. The proposed model per
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