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標(biāo)題: Titlebook: High-Dimensional and Low-Quality Visual Information Processing; From Structured Sens Yue Deng Book 2015 Springer-Verlag Berlin Heidelberg 2 [打印本頁(yè)]

作者: Herbaceous    時(shí)間: 2025-3-21 17:28
書目名稱High-Dimensional and Low-Quality Visual Information Processing影響因子(影響力)




書目名稱High-Dimensional and Low-Quality Visual Information Processing影響因子(影響力)學(xué)科排名




書目名稱High-Dimensional and Low-Quality Visual Information Processing網(wǎng)絡(luò)公開(kāi)度




書目名稱High-Dimensional and Low-Quality Visual Information Processing網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書目名稱High-Dimensional and Low-Quality Visual Information Processing被引頻次




書目名稱High-Dimensional and Low-Quality Visual Information Processing被引頻次學(xué)科排名




書目名稱High-Dimensional and Low-Quality Visual Information Processing年度引用




書目名稱High-Dimensional and Low-Quality Visual Information Processing年度引用學(xué)科排名




書目名稱High-Dimensional and Low-Quality Visual Information Processing讀者反饋




書目名稱High-Dimensional and Low-Quality Visual Information Processing讀者反饋學(xué)科排名





作者: FELON    時(shí)間: 2025-3-21 23:09

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作者: 范例    時(shí)間: 2025-3-23 13:39

作者: Chronic    時(shí)間: 2025-3-23 17:23
Yue Deng, electromagnetics, mathematical finance, biomedical enginee.The present volume is comprised of contributions solicited from invitees to conferences held at the University of Houston, Jyv?skyl? University, and Xi’an Jiaotong University honoring the 70th birthday of Professor Roland Glowinski. Althou
作者: llibretto    時(shí)間: 2025-3-23 18:49

作者: Control-Group    時(shí)間: 2025-3-24 00:19

作者: tic-douloureux    時(shí)間: 2025-3-24 05:44
Yue DengXi’an Jiaotong University honoring the 70th birthday of Professor Roland Glowinski. Although scientists convened on three different continents, the Editors prefer to view the meetings as single event. The three locales signify the fact Roland has friends, collaborators and admirers across the globe.
作者: 試驗(yàn)    時(shí)間: 2025-3-24 10:21

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作者: 香料    時(shí)間: 2025-3-24 16:28
Introduction,n processing and indicate the irresistible trend of their marriage in this big data era. After introducing the low-quality properties in visual data, it will be apparent why computational methods provide an effective way to cope with these defects in visual information processing. Then, four differe
作者: 不能逃避    時(shí)間: 2025-3-24 19:02
Sparse Structure for Visual Information Sensing: Theory and Algorithms,f compressive sensing, we will discuss the problem of low-rank structure learning (LRSL) from sparse outliers. Different from traditional approaches, which directly utilize convex norms to measure the sparseness, our method introduces more reasonable non-convex measurements to enhance the sparsity i
作者: Indurate    時(shí)間: 2025-3-25 00:53
Sparse Structure for Visual Signal Sensing: Application in 3D Reconstruction,m using a low rank structure learning model proposed in last chapter. With this framework, we construct the initial incomplete matrix from the observed point clouds by all cameras, with the invisible points by any camera denoted as unknown entries. The observed points corresponding to the same objec
作者: 嬉耍    時(shí)間: 2025-3-25 03:30
Graph Structure for Visual Signal Sensing,raction method called . (CTG) in the graph embedding framework. We introduce the usage of a robust probability metric, i.e., the commute time (CT), to extract visual features for face recognition via a manifold way. Then, we design the CTG optimization to find linear orthogonal projections that woul
作者: 傾聽(tīng)    時(shí)間: 2025-3-25 09:02
Discriminative Structure for Visual Signal Understanding,our method can be concluded as that differences among multiple images help visual recognition. Generally speaking, we propose a statistical framework to distinguish what kind of image features capture sufficient category information and what kind of image features are common ones shared in multiple
作者: Heresy    時(shí)間: 2025-3-25 12:21

作者: Increment    時(shí)間: 2025-3-25 18:44
Conclusion,dressed one topic (data sensing), discussed two computational frameworks (optimization and probabilistic inference), and coped with three “l(fā)ow-quality” drawbacks (redundancy, noise, and incompleteness).
作者: 同來(lái)核對(duì)    時(shí)間: 2025-3-25 19:58

作者: Malaise    時(shí)間: 2025-3-26 01:18
High-Dimensional and Low-Quality Visual Information Processing978-3-662-44526-6Series ISSN 2190-5053 Series E-ISSN 2190-5061
作者: troponins    時(shí)間: 2025-3-26 04:52
Yue DengNominated by Tsinghua University as an outstanding Ph.D. thesis.Proposes a number of computational models to handle the Big Data challenges in visual information processing.Solves a number of real-wor
作者: Awning    時(shí)間: 2025-3-26 08:38
Springer Theseshttp://image.papertrans.cn/h/image/426572.jpg
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作者: 嚴(yán)峻考驗(yàn)    時(shí)間: 2025-3-26 18:14
https://doi.org/10.1007/978-3-662-44526-6Compressive Sensing; Computer Vision; Discriminative Learning, Information Theory, Optimization; Image
作者: optic-nerve    時(shí)間: 2025-3-27 00:23
Discriminative Structure for Visual Signal Understanding,es help visual recognition within the bag-of-feature framework. The proposed method is verified on a variety of image categorization tasks including outdoor scene images, indoor scene images as well as the airborne SAR images from different perspectives.
作者: Maximize    時(shí)間: 2025-3-27 02:25
Information-Theoretic Structure for Visual Signal Understanding,ph metric. Meanwhile, the mutual information of descriptor-label pairs is ultimately optimized in the embedding space for the sake of enhancing the discriminant property of the selected codewords. According to such objective, two optimization models, i.e., inexact-GAMI and exact-GAMI, are, respectiv
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作者: Arboreal    時(shí)間: 2025-3-27 12:36

作者: CBC471    時(shí)間: 2025-3-27 17:03
Yue Dengid mechanics, numerical controls for distributed parameter systems, and solid and structural mechanics as well as shape optimization, stellar motion, electron transport, and semiconductor modeling. Two central 978-94-007-3128-8978-90-481-3239-3Series ISSN 1871-3033 Series E-ISSN 2543-0203
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作者: 范例    時(shí)間: 2025-3-28 09:36
Yue Denggy. He has made seminal contributions in the areas of methods for science computation, fluid mechanics, numerical controls for distributed parameter systems, and solid and structural mechanics as well as shape optimization, stellar motion, electron transport, and semiconductor modeling. Two central




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