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Titlebook: MultiMedia Modeling; 30th International C Stevan Rudinac,Alan Hanjalic,Yoko Yamakata Conference proceedings 2024 The Editor(s) (if applicab

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書目名稱MultiMedia Modeling
副標(biāo)題30th International C
編輯Stevan Rudinac,Alan Hanjalic,Yoko Yamakata
視頻videohttp://file.papertrans.cn/641/640134/640134.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: MultiMedia Modeling; 30th International C Stevan Rudinac,Alan Hanjalic,Yoko Yamakata Conference proceedings 2024 The Editor(s) (if applicab
描述.This book constitutes the refereed proceedings of the?30th International Conference on MultiMedia Modeling, MMM 2024, held in Amsterdam, The Netherlands, during January 29–February 2, 2024..The 112 full papers included in this volume were carefully reviewed and selected from 297 submissions. The MMM conference were organized in topics related to multimedia modelling, particularly: audio, image, video processing, coding and compression; multimodal analysis for retrieval applications, and multimedia fusion methods..
出版日期Conference proceedings 2024
關(guān)鍵詞databases; education; engineering; image analysis; image processing; image quality; image reconstruction; i
版次1
doihttps://doi.org/10.1007/978-3-031-53311-2
isbn_softcover978-3-031-53310-5
isbn_ebook978-3-031-53311-2Series 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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Conference proceedings 2024nds, during January 29–February 2, 2024..The 112 full papers included in this volume were carefully reviewed and selected from 297 submissions. The MMM conference were organized in topics related to multimedia modelling, particularly: audio, image, video processing, coding and compression; multimoda
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Pharmacogenetics in Lung Cancer,al growth factor receptor (EGFR) inhibitors have been licensed for use in NSCLC. This chapter will focus on these new agents and how molecular profiling on . and . mutation is informative in selecting patients most likely to respond.
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