中国科学院机构知识库网格系统: 检索

Rapid origin discrimination of Meconopsis integrifolia coupled with machine learning techniques and multi-spectral data fusion strategies 期刊论文 OAI收割
INDUSTRIAL CROPS AND PRODUCTS, 2025, 卷号: 229
作者: Sun, Jing; Li, Jiamin; Li, Duo; Gao, Xiuzhen; Li, Hongmei

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收藏 | 浏览/下载：11/0 | 提交时间：2025/11/27

Origin discrimination Multi-infrared spectral data fusion strategy Meconopsis integrifolia Machine learning techniques

DPF-Net: A Dual-Path Progressive Fusion Network for Retinal Vessel Segmentation 期刊论文 OAI收割
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2023, 卷号: 72, 页码: 17
作者: Li, Jianyong; Gao, Ge; Yang, Lei; Bian, Guibin; Liu, Yanhong

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收藏 | 浏览/下载：51/0 | 提交时间：2023/11/17

Feature extraction Image segmentation Retinal vessels Biomedical imaging Blood vessels Convolutional neural networks Task analysis Deep network progressive fusion strategy retinal vessel segmentation semantic segmentation U-shape network

An improved DC component suppression control strategy applied to high voltage power supply for N-NBI accelerator grid 期刊论文 OAI收割
ENERGY REPORTS, 2022, 卷号: 8
作者: Li, Xueshan; Huang, Yiyun; Gao, Zongqiu; Pan, Shengming; Lu, Wei

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收藏 | 浏览/下载：49/0 | 提交时间：2022/12/22

Nuclear fusion Isolated DCDC converter Inverter DC component suppression Kalman filter Control strategy

Single UWB Anchor Aided PDR Heading and Step Length Correcting Indoor Localization System 期刊论文 OAI收割
IEEE ACCESS, IEEE ACCESS, 2021, 2021, 卷号: 9, 9, 页码: 11511-11522, 11511-11522
作者: Long, Keliu; Shen, Chong; Tian, Chuan; Zhang, Kun; Bhatti, Uzair Aslam

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收藏 | 浏览/下载：53/0 | 提交时间：2021/03/23

Location awareness Location awareness Distance measurement Legged locomotion Estimation Gyroscopes Filtering algorithms Kalman filters Pedestrian dead reckon ultra-wide band indoor localization fusion strategy Distance measurement Legged locomotion Estimation Gyroscopes Filtering algorithms Kalman filters Pedestrian dead reckon ultra-wide band indoor localization fusion strategy

Hierarchical Feature Fusion Network for Salient Object Detection 期刊论文 OAI收割
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 卷号: 29, 页码: 9165-9175
作者: Li, Xuelong; Song, Dawei; Dong, Yongsheng

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收藏 | 浏览/下载：33/0 | 提交时间：2020/10/30

Feature extraction Semantics Image edge detection Object detection Fuses Visualization Image color analysis Salient object detection hierarchical feature fusion edge information-guided one-to-one hierarchical supervision strategy

Beyond global fusion: A group-aware fusion approach for multi-view image clustering 期刊论文 OAI收割
INFORMATION SCIENCES, 2019, 卷号: 493, 页码: 176-191
作者: Wang, Shuhui; Huang, Jun; Zhang, Weigang; Huang, Qingming; Xue, Zhe

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收藏 | 浏览/下载：84/0 | 提交时间：2019/08/16

Multi-view learning Local fusion strategy Group-aware fusion Image clustering

Combined optimisation of waveform and quantisation thresholds for multistatic radar systems 期刊论文 OAI收割
IET SIGNAL PROCESSING, 2018, 卷号: 12, 期号: 5, 页码: 559-565
作者: Zhu, Jiang; Ji, Daxiong; Xu, Zhiwei; Si, Bailu

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收藏 | 浏览/下载：41/0 | 提交时间：2021/02/02

numerical analysis data compression Gaussian noise radar signal processing quantisation (signal) optimisation combined waveform-quantisation threshold optimisation strategy distributed receivers fusion centre capacity constraints cloud radio-multistatic radar system additive quantisation Gaussian noise model real quantisation system received signal compression scalar quantiser numerical simulations

Unsupervised Salient Object Detection via Inferring From Imperfect Saliency Models 期刊论文 OAI收割
IEEE TRANSACTIONS ON MULTIMEDIA, 2018, 卷号: 20, 期号: 5, 页码: 1101-1112
作者: Quan, Rong; Han, Junwei; Zhang, Dingwen; Nie, Feiping; Qian, Xueming

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收藏 | 浏览/下载：66/0 | 提交时间：2018/05/14

Salient Object Detection Weak Prediction Fusion Strategy Local Spatial Consistency Constraint

Enhancing person re-identification by integrating gait biometric 期刊论文 OAI收割
Neurocomputing, 2014, 卷号: 168, 期号: 1, 页码: 1144-1156
作者: Zheng Liu; Zhaoxiang Zhang; Qiang Wu; Yunhong Wang

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收藏 | 浏览/下载：39/0 | 提交时间：2017/02/09

Person Re-identification Visual Surveillance Gait Fusion Strategy Metric Learning

The new approach for infrared target tracking based on the particle filter algorithm (EI CONFERENCE) 会议论文 OAI收割
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications, May 24, 2011 - May 24, 2011, Beijing, China
作者: Sun H.; Han H.-X.

收藏 | 浏览/下载：79/0 | 提交时间：2013/03/25

Target tracking on the complex background in the infrared image sequence is hot research field. It provides the important basis in some fields such as video monitoring precision and video compression human-computer interaction. As a typical algorithms in the target tracking framework based on filtering and data connection the particle filter with non-parameter estimation characteristic have ability to deal with nonlinear and non-Gaussian problems so it were widely used. There are various forms of density in the particle filter algorithm to make it valid when target occlusion occurred or recover tracking back from failure in track procedure but in order to capture the change of the state space it need a certain amount of particles to ensure samples is enough and this number will increase in accompany with dimension and increase exponentially this led to the increased amount of calculation is presented. In this paper particle filter algorithm and the Mean shift will be combined. Aiming at deficiencies of the classic mean shift Tracking algorithm easily trapped into local minima and Unable to get global optimal under the complex background. From these two perspectives that "adaptive multiple information fusion" and "with particle filter framework combining" we expand the classic Mean Shift tracking framework.Based on the previous perspective we proposed an improved Mean Shift infrared target tracking algorithm based on multiple information fusion. In the analysis of the infrared characteristics of target basis Algorithm firstly extracted target gray and edge character and Proposed to guide the above two characteristics by the moving of the target information thus we can get new sports guide grayscale characteristics and motion guide border feature. Then proposes a new adaptive fusion mechanism used these two new information adaptive to integrate into the Mean Shift tracking framework. Finally we designed a kind of automatic target model updating strategy to further improve tracking performance. Experimental results show that this algorithm can compensate shortcoming of the particle filter has too much computation and can effectively overcome the fault that mean shift is easy to fall into local extreme value instead of global maximum value.Last because of the gray and fusion target motion information this approach also inhibit interference from the background ultimately improve the stability and the real-time of the target track. 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).