中国科学院机构知识库网格系统: 检索

Nonlinear Filtering With Sample-Based Approximation Under Constrained Communication: Progress, Insights and Trends 期刊论文 OAI收割
IEEE/CAA Journal of Automatica Sinica, 2024, 卷号: 11, 期号: 7, 页码: 1539-1556
作者: Weihao Song; Zidong Wang; Zhongkui Li; Jianan Wang; Qing-Long Han

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收藏 | 浏览/下载：9/0 | 提交时间：2024/06/07

Communication constraints maximum correntropy filter networked nonlinear filtering particle filter sample-based approximation unscented Kalman filter

Visual group target tracking algorithm based on MeanShift-PCA-PF 会议论文 OAI收割
Hybrid, Xi'an, China, 2023-04-21
作者: Li, Jianing; Tian, Yan; Guo, Min; Zuo, Kaige; Wang, Xin

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收藏 | 浏览/下载：17/0 | 提交时间：2023/11/09

group targets target tracking clustering detection particle filtering

Robust correlation filter tracking with deep semantic supervision 期刊论文 OAI收割
IET IMAGE PROCESSING, 2019, 卷号: 13, 期号: 5, 页码: 754-760
作者: Wang, Wei; Chen, Zhaoming; Douadji, Lyes; Shi, Mingquan

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收藏 | 浏览/下载：86/0 | 提交时间：2019/06/24

particle filtering (numerical methods) learning (artificial intelligence) target tracking convolutional neural nets robust correlation filter tracking high tracking performance tracking failure deep semantic supervision tracking framework redetection tracking mechanism particle filtering resampling CF tracker deep convolutional neural network tracking frames target occlusion handcrafted features real-time performance OTB2013 benchmark datasets OTB2015 benchmark datasets

Sparse solution of PP-PS joint inversion with constraint of particle filtering 期刊论文 OAI收割
CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION, 2018, 卷号: 61, 期号: 3, 页码: 1169-1177
作者: Wang YanFei; Tang Jing; Geng WeiFeng

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收藏 | 浏览/下载：27/0 | 提交时间：2018/09/26

Joint inversion Particle filtering Regularization l(1) norm Sparse solution

Sparse solution of PP-PS joint inversion with constraint of particle filtering 期刊论文 OAI收割
CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION, 2018, 卷号: 61, 期号: 3, 页码: 1169-1177
作者: Wang YanFei; Tang Jing; Geng WeiFeng; Wang ChengXiang

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收藏 | 浏览/下载：33/0 | 提交时间：2018/09/26

Joint inversion Particle filtering Regularization l(1) norm Sparse solution

PP and PS joint inversion with a posterior constraint and with particle filtering 期刊论文 OAI收割
JOURNAL OF GEOPHYSICS AND ENGINEERING, 2017, 卷号: 14, 期号: 6, 页码: 1399-1412
作者: Tang, Jing; Wang, Yanfei

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收藏 | 浏览/下载：32/0 | 提交时间：2017/11/24

joint inversion regularization particle filtering

A Parallel Markov Cerebrovascular Segmentation Algorithm Based on Statistical Model 期刊论文 OAI收割
JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, 2016, 卷号: 31, 期号: 2, 页码: 400-416
作者: Cao, Rong-Fei; Wang, Xing-Ce; Wu, Zhong-Ke; Zhou, Ming-Quan; Liu, Xin-Yu

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收藏 | 浏览/下载：21/0 | 提交时间：2019/12/13

cerebrovascular segmentation non-local means filtering Markov random field particle swarm optimization algorithm

Chaos particle swarm optimization combined with circular median filtering for geophysical parameters retrieval from Windsat 期刊论文 OAI收割
JOURNAL OF OCEAN UNIVERSITY OF CHINA, 2016, 卷号: 15, 期号: 4, 页码: 593-605
作者: Zhang Lei; Wang Zhenzhan; Shi Hanqing; Long Zhiyong; Du Huadong

收藏 | 浏览/下载：32/0 | 提交时间：2016/11/08

WindSat polarimetric microwave radiometer wind retrieval chaotic particle swarm optimization circular median filtering

Efficient Object Tracking by Incremental Self-Tuning Particle Filtering on the Affine Group 期刊论文 OAI收割
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2012, 卷号: 21, 期号: 3, 页码: 1298-1313
作者: Li, Min; Tan, Tieniu; Chen, Wei; Huang, Kaiqi

收藏 | 浏览/下载：17/0 | 提交时间：2015/08/12

Incremental self-tuning particle filtering (ISPF) pose estimator (PE) sparse sampling visual tracking

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：57/0 | 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).