机构

• 自动化研究所 [4]
• 长春光学精密机械与物... [2]
• 合肥物质科学研究院 [2]
• 计算技术研究所 [1]
• 心理研究所 [1]
• 软件研究所 [1]
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采集方式

• OAI收割 [11]

内容类型

• 期刊论文 [7]
• 会议论文 [3]
• 学位论文 [1]

发表日期

• 2024 [1]
• 2023 [1]
• 2022 [1]
• 2019 [1]
• 2017 [1]
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学科主题

• 安全科学技术 [1]
• 核科学技术 [1]

筛选

浏览/检索结果: 共11条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 Adaptive-weighted deep multi-view clustering with uniform scale representation 期刊论文  OAI收割 NEURAL NETWORKS, 2024, 卷号: 171, 页码: 114-126 作者:  Chen, Rui;  Tang, Yongqiang;  Zhang, Wensheng;  Feng, Wenlong   |  收藏  |  浏览/下载：28/0  |  提交时间：2024/02/21 Multi-view clustering  Deep clustering  Adaptive-weighted learning  Uniform scale representation   Depth-Aware Multi-Person 3D Pose Estimation With Multi-Scale Waterfall Representations 期刊论文  OAI收割 IEEE TRANSACTIONS ON MULTIMEDIA, 2023, 卷号: 25, 页码: 1439-1451 作者:  Shen, Tianyu;  Li, Deqi   |  收藏  |  浏览/下载：10/0  |  提交时间：2023/11/17 Three-dimensional displays  Pose estimation  Feature extraction  Location awareness  Cameras  Semantics  Solid modeling  Human depth perceiving  multi-person 3d pose estimation  multi-scale representation  occlusion handling   A Deep Network Based on Wavelet Transform for Image Compressed Sensing 期刊论文  OAI收割 CIRCUITS SYSTEMS AND SIGNAL PROCESSING, 2022 作者:  Yin, Zhu;  Wu, Zhongcheng;  Zhang, Jun   |  收藏  |  浏览/下载：33/0  |  提交时间：2022/12/23 Compressed sensing  Sparse representation  Sampling network  Multi-scale residual  Reconstruction network   Tattoo Image Search at Scale: Joint Detection and Compact Representation Learning 期刊论文  OAI收割 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2019, 卷号: 41, 期号: 10, 页码: 2333-2348 作者:  Han, Hu;  Li, Jie;  Jain, Anil K.;  Shan, Shiguang;  Chen, Xilin   |  收藏  |  浏览/下载：77/0  |  提交时间：2019/12/10 Large-scale tattoo search  joint detection and representation learning  sketch based search  multi-task learning   MFC: A Multi-scale Fully Convolutional Approach for Visual Instance Retrieval 会议论文  OAI收割 Hong Kong, 10-14 July 2017 作者:  Hao, Jiedong;  Wang, Wei;  Dong, Jing;  Tan, Tieniu   |  收藏  |  浏览/下载：23/0  |  提交时间：2018/05/31 Visual Instance Retrieval  Image Resizing Strategy  Multi-scale Representation  Fully Convolutional Neural Network   Visualizing and Analyzing Video Content With Interactive Scalable Maps 期刊论文  OAI收割 IEEE TRANSACTIONS ON MULTIMEDIA, 2016, 卷号: 18, 期号: 11, 页码: 2171-2183 作者:  Ma, Cui-Xia;  Liu, Yong-Jin;  Zhao, Guozhen;  Wang, Hong-An 收藏  |  浏览/下载：26/0  |  提交时间：2016/12/26 Interaction  map metaphor  multi-scale representation  video visualization and analysis   Visualizing and Analyzing Video Content With Interactive Scalable Maps 期刊论文  OAI收割 IEEE TRANSACTIONS ON MULTIMEDIA, 2016, 卷号: 18, 期号: 11, 页码: 2171-2183 Ma, CX; Liu, YJ; Zhao, GZ; Wang, HA   |  收藏  |  浏览/下载：23/0  |  提交时间：2016/12/09 Interaction  map metaphor  multi-scale representation  video visualization and analysis   指纹识别系统中假指纹检测技术的研究 学位论文  OAI收割 工学博士, 中国科学院自动化研究所: 中国科学院大学, 2015 作者:  贾晓飞 收藏  |  浏览/下载：46/0  |  提交时间：2015/09/02 系统安全性  假指纹检测  稀疏表示  多尺度局部二模式模型  未知材料制作的假指纹  System Security  Fake Fingerprint Detection  Sparse Representation  Multi-scale LBP  Artifacts Made of Unknown Materials   Angular Pattern and Binary Angular Pattern for Shape Retrieval 期刊论文  OAI收割 ieee transactions on image processing, 2014, 卷号: 23, 期号: 3, 页码: 1118-1127 作者:  Rong-Xiang Hu;  Wei Jia;  Haibin Ling;  Yang Zhao;  Jie Gui 收藏  |  浏览/下载：20/0  |  提交时间：2015/12/30 Shape retrieval  angular features  binary pattern  multi-scale representation   Image coding using wavelet-based compressive sampling (EI CONFERENCE) 会议论文  OAI收割 2012 5th International Symposium on Computational Intelligence and Design, ISCID 2012, October 28, 2012 - October 29, 2012, Hangzhou, China 作者:  Li J.;  Li J.;  Li J. 收藏  |  浏览/下载：45/0  |  提交时间：2013/03/25 In this paper  we proposed a novel coding scheme is proposed using wavelet-based CS framework for nature image. First  two-dimension discrete wavelet transform (DWT) is applied to a nature image for sparse representation. After multi-scale DWT  the low-frequency sub-band and high-frequency sub-bands are re-sampled separately. According to the statistical dependences among DWT coefficients  we allocate different measurements to low- and high-frequency component. Then  the measurements samples can be quantized. The quantize samples are entropy coded and forward correct coding (FEC). Finally  the compressed streams are transmitted. At the decoder  one can simply reconstruct the image via l1 minimization. Experimental results show that the proposed wavelet-based CS scheme achieves better compression performance against the relevant existing solutions.