中国科学院机构知识库网格系统: 检索

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	Image Representations With Spatial Object-to-Object Relations for RGB-D Scene Recognition 期刊论文 OAI收割 IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 卷号: 29, 页码: 525-537 作者: Song, Xinhang; Jiang, Shuqiang; Wang, Bohan; Chen, Chengpeng; Chen, Gongwei \| 收藏 \| 浏览/下载：42/0 \| 提交时间：2020/12/10 Feature extraction Object detection Image recognition Layout Data models Recurrent neural networks Scene recognition object-to-object relation sequential representations RGB-D object detection
	Feature Aggregation With Reinforcement Learning for Video-Based Person Re-Identification 期刊论文 OAI收割 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2019, 卷号: 30, 期号: 12, 页码: 3847-3852 作者: Zhang, Wei; He, Xuanyu; Lu, Weizhi; Qiao, Hong; Li, Yibin \| 收藏 \| 浏览/下载：51/0 \| 提交时间：2020/03/30 Feature extraction Task analysis Cameras Noise measurement Learning systems Reinforcement learning Feature aggregation reinforcement learning (RL) sequential decision making video-based person re-identification (re-id)
	Learning sequential features for cascade outbreak prediction 期刊论文 OAI收割 KNOWLEDGE AND INFORMATION SYSTEMS, 2018, 卷号: 57, 期号: 3, 页码: 721-739 作者: Gou, Chengcheng; Shen, Huawei; Du, Pan; Wu, Dayong; Liu, Yue \| 收藏 \| 浏览/下载：61/0 \| 提交时间：2019/12/10 Social network Outbreak prediction Sequential feature LSTM Popularity prediction
	Action recognition using spatial-optical data organization and sequential learning framework 期刊论文 OAI收割 NEUROCOMPUTING, 2018, 卷号: 315, 页码: 221-233 作者: Yuan, Yuan; Zhao, Yang; Wang, Qi \| 收藏 \| 浏览/下载：38/0 \| 提交时间：2018/10/31 Action Recognition Spatiotemporal Feature Deep Learning Sequential Learning Framework
	Feature Adaptive Online Sequential Extreme Learning Machine for lifelong indoor localization 期刊论文 OAI收割 NEURAL COMPUTING & APPLICATIONS, 2016, 卷号: 27, 期号: 1, 页码: 215-225 作者: Jiang, Xinlong; Liu, Junfa; Chen, Yiqiang; Liu, Dingjun; Gu, Yang \| 收藏 \| 浏览/下载：17/0 \| 提交时间：2019/12/13 Feature adaptive Online Sequential Extreme Learning Machine (OS-ELM) Lifelong Indoor localization
	Beyond semantic attributes: Auxiliary feature discovery for image classification 期刊论文 OAI收割 NEUROCOMPUTING, 2014, 卷号: 142, 页码: 155-164 作者: Niu, Biao; Cheng, Jian; Liu, Yang; Lu, Hanging; Lu, Hanqing 收藏 \| 浏览/下载：26/0 \| 提交时间：2015/08/12 Augmented feature Sequential feature learning Attribute
	Study particle filter tracking and detection algorithms based on DSP signal processors (EI CONFERENCE) 会议论文 OAI收割 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, CMCE 2010, August 24, 2010 - August 26, 2010, Changchun, China Dong Y.; Chuan W. 收藏 \| 浏览/下载：19/0 \| 提交时间：2013/03/25 In Video tracking detection and tracking usually need two algorithms. The process is complex and need much time which detection and tracking are. In this paper a hybrid valued sequential state vector is formulated. The state vector is characterized by information of target appearance flag and of location. Particle filter-based method implements detection and tracking at one time. In order to reduce process time and think of pixel position in tracking field feature histogram of luminance is as observe vector and used posterior estimate. In this paper the luminance component is derived and target is recognized and tracked through image processor based on DSP in order to implementing real-time. The experimental results confirm that method can detect and track the object in real-time successfully when the number of particles is 160. The method is robust for rolling scale and partial occlusion. 2010 IEEE.
	Study on color image tracking and detection algorithms based on particle filter (EI CONFERENCE) 会议论文 OAI收割 International Symposium on Photoelectronic Detection and Imaging 2009: Advances in Infrared Imaging and Applications, June 17, 2009 - June 19, 2009, Beijing, China Wu C.; Sun H.-J.; Yang D. 收藏 \| 浏览/下载：20/0 \| 提交时间：2013/03/25 In Video tracking detection and tracking need two algorithms. The process is complex and need much time which detection and tracking is. In this paper a hybrid valued sequential state vector is formulated. The state vector is characterized by information of target appearance and of location. Particle filter-based method implements detection and tracking. In order to reduce process time and think of pixel position in tracking field feature histogram of color-based is as observe vector and used posterior estimate. The experimental results confirm that method can detect and track object in 17.68ms successfully when the number of particles is 160. The method is robust for rolling scale and partial occlusion. 2009 SPIE.
	Tracking Deformable Object via Particle Filtering on Manifolds 会议论文 OAI收割 Chinese Conference one Pattern Recognition, Beijing, China, December 22-24, 2008 作者: Liu YP(刘云鹏); Li GW(李广伟); Shi ZL(史泽林) 收藏 \| 浏览/下载：19/0 \| 提交时间：2012/06/06 target tracking Sequential Monte Carlo Manifolds Lie Group Gabor feature