中国科学院机构知识库网格系统: 检索

Uncertainty-Aware Dual-Evidential Learning for Weakly-Supervised Temporal Action Localization 期刊论文 OAI收割
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2023, 卷号: 45, 期号: 12, 页码: 15896-15911
作者: Chen, Mengyuan; Gao, Junyu; Xu, Changsheng

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收藏 | 浏览/下载：24/0 | 提交时间：2024/03/26

Uncertainty Background noise Task analysis Location awareness Measurement uncertainty Interference Predictive models Weakly-supervised temporal action localization evidential deep learning uncertainty estimation

Estimating winter wheat yield by assimilation of remote sensing data with a four-dimensional variation algorithm considering anisotropic background error and time window 期刊论文 OAI收割
AGRICULTURAL AND FOREST METEOROLOGY, 2021, 卷号: 301, 页码: 16
作者: Wu, Shangrong; Yang, Peng; Chen, Zhongxin; Ren, Jianqiang; Li, He

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收藏 | 浏览/下载：51/0 | 提交时间：2021/06/10

Data assimilation Crop yield estimation Remotely sensed LAI Background error Time window Anisotropic

Unsupervised Domain Adaptation with Background Shift Mitigating for Person Re-Identification 期刊论文 OAI收割
INTERNATIONAL JOURNAL OF COMPUTER VISION, 2021, 页码: 20
作者: Huang, Yan; Wu, Qiang; Xu, Jingsong; Zhong, Yi; Zhang, Zhaoxiang

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收藏 | 浏览/下载：74/0 | 提交时间：2021/08/15

Person re-identification Unsupervised domain adaptation Background suppression Image generation Virtual label estimation

Space Debris Detection Using Feature Learning of Candidate Regions in Optical Image Sequences 期刊论文 OAI收割
IEEE ACCESS, 2020, 卷号: 8, 页码: 150864-150877
作者: Xi, Jiangbo; Xiang, Yaobing; Ersoy, Okan K.; Cong, Ming; Wei, Xin

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收藏 | 浏览/下载：41/0 | 提交时间：2020/10/23

Space debris Feature extraction Machine learning Signal to noise ratio Object detection Image sequences Optical imaging Space debris detection background estimation candidate region extraction deep learning

Background Subtraction With Real-Time Semantic Segmentation 期刊论文 OAI收割
Ieee Access, 2019, 卷号: 7, 页码: 153869-153884
作者: D.D.Zeng; X.Chen; M.Zhu; M.Goesele; A.Kuijper

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收藏 | 浏览/下载：30/0 | 提交时间：2020/08/24

Background subtraction,foreground object detection,semantic,segmentation,video surveillance,density-estimation,Computer Science,Engineering,Telecommunications

Small target detection based on reweighted infrared patch-image model 期刊论文 OAI收割
IET IMAGE PROCESSING, 2018, 卷号: 12, 期号: 1, 页码: 70-79
作者: Guo, Jun; Wu, Yiquan; Dai, Yimian

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收藏 | 浏览/下载：73/0 | 提交时间：2018/12/12

Object Detection Infrared Imaging Principal Component Analysis Small Target Detection Reweighted Infrared Patch-image Model Infrared Small Target Detection Sparse Background Edges Background Estimation Reweighted Nuclear Norm Nontarget Sparse Points Reweighted Robust Principal Component Analysis Problem Inexact Augmented Lagrangian Multiplier Method Background Clutter Suppression Reweighted l(1) Norm

Background Subtraction Using Multiscale Fully Convolutional Network 期刊论文 OAI收割
Ieee Access, 2018, 卷号: 6, 页码: 16010-16021
作者: Zeng, D. D.; Zhu, M.

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收藏 | 浏览/下载：21/0 | 提交时间：2019/09/17

Background subtraction convolutional neural network multiscale fully convolutional network video surveillance density-estimation Computer Science Engineering Telecommunications

Combining depth and gray images for fast 3D object recognition 会议论文 OAI收割
International Symposium on Optical Measurement Technology and Instrumentation, Beijing, China, May 9-11, 2016
作者: Pan W(潘旺); Zhu F(朱枫); Hao YM(郝颖明)

收藏 | 浏览/下载：17/0 | 提交时间：2017/01/14

Depth Image 3D object recognition pose estimation machine vision robotics background elimination shape matching

基于背景估计和边缘检测的文档图像二值化 期刊论文 OAI收割
计算机应用与软件, 2014, 卷号: 31, 期号: 8, 页码: 196-200
许海洋; 马龙龙; 吴健

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收藏 | 浏览/下载：41/0 | 提交时间：2014/12/16

文档图像二值化背景估计边缘检测 Canny 局部阈值法 Document image Binarisation Background estimation Edge detection Canny Local threshold method

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.; Sun H.

收藏 | 浏览/下载：59/0 | 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).