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Titlebook: Intelligence Science and Big Data Engineering. Visual Data Engineering; 9th International Co Zhen Cui,Jinshan Pan,Jian Yang Conference proc

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发表于 2025-3-21 20:02:52 | 显示全部楼层 |阅读模式
书目名称Intelligence Science and Big Data Engineering. Visual Data Engineering
副标题9th International Co
编辑Zhen Cui,Jinshan Pan,Jian Yang
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Intelligence Science and Big Data Engineering. Visual Data Engineering; 9th International Co Zhen Cui,Jinshan Pan,Jian Yang Conference proc
描述.The two volumes LNCS 11935 and 11936 constitute the proceedings of the 9th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2019, held in Nanjing, China, in October 2019...The  84 full papers presented were carefully reviewed and selected from 252 submissions.The papers are organized in two parts: visual data engineering; and big data and machine learning. They cover a large range of topics including information theoretic and Bayesian approaches, probabilistic graphical models, big data analysis, neural networks and neuro-informatics, bioinformatics, computational biology and brain-computer interfaces, as well as advances in fundamental pattern recognition techniques relevant to image processing, computer vision and machine learning... .
出版日期Conference proceedings 2019
关键词artificial intelligence; computational linguistics; computer networks; computer vision; data mining; face
版次1
doihttps://doi.org/10.1007/978-3-030-36189-1
isbn_softcover978-3-030-36188-4
isbn_ebook978-3-030-36189-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

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发表于 2025-3-21 20:17:42 | 显示全部楼层
Intelligence Science and Big Data Engineering. Visual Data Engineering978-3-030-36189-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
发表于 2025-3-22 01:19:55 | 显示全部楼层
https://doi.org/10.1007/978-3-030-36189-1artificial intelligence; computational linguistics; computer networks; computer vision; data mining; face
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Adaptive Online Learning for Video Object Segmentation,arn how to online adapt the learned segmentation model to the specific testing video sequence and the corresponding future video frames, where the confidence patterns is employed to constrain/guide the implementation of adaptive learning process by fusing both object appearance and motion cue inform
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Memory Network-Based Quality Normalization of Magnetic Resonance Images for Brain Segmentation, MemNet-based algorithm can not only normalize and improve the quality of brain MR images, but also enable the same 3D U-Net to produce substantially more accurate segmentation of major brain tissues.
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Sparse-Temporal Segment Network for Action Recognition,ontain the complementary features. Extensive experiments in subjective and objective show that temporal-sparse segment network can reach the accuracy of 94.2%, which is significantly better than several state-of-the-art algorithms.
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