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Titlebook: Image and Video Technology; 8th Pacific-Rim Symp Manoranjan Paul,Carlos Hitoshi,Qingming Huang Conference proceedings 2018 Springer Nature

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发表于 2025-3-21 18:49:50 | 显示全部楼层 |阅读模式
书目名称Image and Video Technology
副标题8th Pacific-Rim Symp
编辑Manoranjan Paul,Carlos Hitoshi,Qingming Huang
视频videohttp://file.papertrans.cn/462/461524/461524.mp4
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Image and Video Technology; 8th Pacific-Rim Symp Manoranjan Paul,Carlos Hitoshi,Qingming Huang Conference proceedings 2018 Springer Nature
描述This book constitutes the thoroughly refereed post-conference proceedings of the 8th Pacific Rim Symposium on Image and Video Technology, PSIVT 2017, held in Wuhan, China, in November 2017..The total of 39 revised papers was carefully reviewed and selected from 91 submissions..The Pacific-Rim Symposium on Image and VideoTechnology (PSIVT) is a high-quality series of symposia that aim atproviding a forum for researchers and practitioners who are beinginvolved, or are contributing totheoretical advances or practicalimplementations in image and video technology..
出版日期Conference proceedings 2018
关键词Image processing; Video processing; Image Analysis; Video analysis; Pattern recognition; Applications of
版次1
doihttps://doi.org/10.1007/978-3-319-75786-5
isbn_softcover978-3-319-75785-8
isbn_ebook978-3-319-75786-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2018
The information of publication is updating

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Rim Trabelsi,Issam Jabri,Farid Melgani,Fethi Smach,Nicola Conci,Ammar BouallegueSCO’s role in the realm of books, libraries, and networks of information exchange during the early postwar, post-Holocaust, Cold War years..978-3-030-15818-7978-3-030-15816-3Series ISSN 2634-6117 Series E-ISSN 2634-6125
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Video Highlight Detection via Deep Ranking Modelinge proposed attention model produces attention weights of local regions along both the spatial and temporal dimensions of the video segment. The regions of key elements in the video will be strengthened with large weights. Thus more effective feature of the video segment is obtained to predict the hi
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Detection of Adulteration in Red Meat Species Using Hyperspectral Imaginges at all meat stages. The resulting model yields 94% and 90% average sensitivities for detecting lamb or the other meat type, respectively. This paper shows that hyperspectral imaging analysis provides a rapid, reliable, and non-destructive method for detecting the adulteration in red-meat products
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Unsupervised Domain Adaptation with Robust Deep Logistic Regression classifiers trained on source classifier to predict target samples. Thus we deploy a robust deep logistic regression loss on the target samples, resulting in our RDLR model. In such a way, pseudo-labels are gradually assigned to unlabeled target samples according to their maximum classification sco
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