中国科学院机构知识库网格系统: 检索

Major Development Under Gaussian Filtering Since Unscented Kalman Filter 期刊论文 OAI收割
IEEE/CAA Journal of Automatica Sinica, 2020, 卷号: 7, 期号: 5, 页码: 1308-1325
作者: Abhinoy Kumar Singh

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收藏 | 浏览/下载：23/0 | 提交时间：2021/03/11

Bayesian framework cubature rule-based filtering Gaussian filters Gaussian sum and square-root filtering nonlinear filtering quadrature rule-based filtering unscented transformation

Monocular adaptive inverse depth filtering algorithm based on Gaussian model 会议论文 OAI收割
Hefei, China, August 22-24, 2020
作者: Xu, Chenglong; Wu CD(吴成东); Qu DK(曲道奎); Sun HB(孙海波); Song JL(宋吉来)

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收藏 | 浏览/下载：27/0 | 提交时间：2020/10/10

Monocular depth estimation Inverse depth filtering Gaussian model Normalized cross-correlation

Testing the Efficiency of Using High-Resolution Data From GF-1 in Land Cover Classifications 期刊论文 OAI收割
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2018, 卷号: 11, 期号: 9, 页码: 3051-3061
作者: Wang, Xiaofeng; Zhou, Chaowei; Feng, Xiaoming; Cheng, Changwu; Fu, Bojie

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收藏 | 浏览/下载：39/0 | 提交时间：2019/06/17

GF-1 gray-level co-occurrence matrix (GLCM) land cover Laplacian-of-Gaussian (LOG) filtering multisource data classification support vector machine (SVM) the Loess plateau

Tone mapping infrared images using conditional filtering based multi-scale retinex 会议论文 OAI收割
Conference on Applied Optics and Photonics (AOPC) - Image Processing and Analysis, Beijing, MAY 05-07, 2015
作者: Luo HB(罗海波); Xu LY(许凌云); Hui B(惠斌); Chang Z(常铮)

收藏 | 浏览/下载：30/0 | 提交时间：2015/11/18

Tone mapping High dynamic rage image infrared imaging Multi-scale retinex Image enhancement Conditional Gaussian Filter (CGF) Conditional Filtering Based Multi-Scale Retinex(CFBMSR)

perceptual video hashing robust against geometric distortions 期刊论文 OAI收割
SCIENCE CHINA-INFORMATION SCIENCES, 2012, 卷号: 55, 期号: 7, 页码: 1520-1527
Xiang ShiJun; Yang JianQuan; Huang JiWu

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收藏 | 浏览/下载：24/0 | 提交时间：2013/09/17

video hashing geometric distortion histogram Gaussian filtering

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：60/0 | 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).

Adaptive Wiener filtering with Gaussian fitted point spread function in image restoration (EI CONFERENCE) 会议论文 OAI收割
2011 IEEE 2nd International Conference on Software Engineering and Service Science, ICSESS 2011, July 15, 2011 - July 17, 2011, Beijing, China
作者: Yang L.; Zhang X.; Zhang X.; Zhang X.; Yang L.

收藏 | 浏览/下载：44/0 | 提交时间：2013/03/25

In the imaging process of the space remote sensing camera there was degradation phenomenon in the acquired images. In order to reduce the image blur caused by the degradation the remote sensing images were restored to give prominence to the characteristic objects in the images. First the frequency-domain notch filter was adopted to remove strip noises in the images. Then using the ground characters with the knife-edge shape in the images the point spread function of the imaging system was estimated. In order to improve the accuracy the estimated point spread function was corrected with Gaussian fitting method. Finally the images were restored using the adaptive Wiener filtering with the fitted point spread function. Experimental results of the real remote sensing images showed that almost all strip noises in the images were eliminated. After the denoised images were restored its variance and its gray mean gradient increased also its laplacian gradient increased. Restoration with Gaussian fitted point spread function is beneficial to interpreting and analyzing the remote sensing images. After restoration the blur phenomenon of the images is reduced. The characters are highlighted and the visual effect of the images is clearer. 2011 IEEE.

Remote sensing image restoration using estimated point spread function (EI CONFERENCE) 会议论文 OAI收割
2010 International Conference on Information, Networking and Automation, ICINA 2010, October 17, 2010 - October 19, 2010, Kunming, China
作者: Yang L.; Yang L.

收藏 | 浏览/下载：34/0 | 提交时间：2013/03/25

In order to reduce image blur caused by the degradation phenomenon in the imaging process the acquired images of the space remote sensing camera are restored. First the frequency-domain notch filter is adopted to remove strip noises in the images. Then degradation function which is referred to as the point spread function (PSF) of the imaging system is estimated using the knife-edge method. To improve the accuracy of the estimation the estimated PSF is adjusted with Gaussian fitting. Finally the images are restored by Wiener filtering with the fitted PSF. The restoration results of the remote sensing images show that almost all strip noises are eliminated by the notch filter. After denoising and restoration the variance of the remote sensing image worked with in this paper increases 30.979 and the gray mean gradient increases 3.312. Due to Gaussian fitting the accuracy of the PSF estimation is heightened. Image restoration with the final PSF is benefit to interpreting and analyzing the remote sensing images. After restoration the contrasts of the restored images are increased and the visual effects become clearer. 2010 IEEE.

stroke extraction in cartoon images using edge-enhanced isotropic nonlinear filter 会议论文 OAI收割
9th ACM SIGGRAPH International Conference on VR Continuum and Its Applications in Industry, VRCAI 2010, Seoul, Korea, Republic of, 40878
Huang Mengcheng; Yang Meng; Liuy Fang; Wuz En-Hua

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收藏 | 浏览/下载：82/0 | 提交时间：2011/03/31

Algorithms Gaussian distribution Interactive computer graphics Laplace transforms Nonlinear filtering

An evaluation of the nonlinear/non-Gaussian filters for the sequential data assimilation 期刊论文 iSwitch采集
REMOTE SENSING OF ENVIRONMENT, 2008, 卷号: 112, 期号: 4, 页码: 1434-1449
作者: Han, Xujun; Li, Xin

收藏 | 浏览/下载：35/0 | 提交时间：2019/10/08

Bayesian filtering nonlinear/non-Gaussian sequential data assimilation Kalman filter particle filter Lorenz model Monte Carlo methods land surface model microwave remote sensing