中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
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浏览/检索结果: 共19条,第1-10条 帮助

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Automatic P-Phase-Onset-Time-Picking Method of Microseismic Monitoring Signal of Underground Mine Based on Noise Reduction and Multiple Detection Indexes 期刊论文  OAI收割
ENTROPY, 2023, 卷号: 25, 期号: 10, 页码: 16
作者:  
Dai, Rui;  Wang, Yibo;  Zhang, Da;  Ji, Hu
  |  收藏  |  浏览/下载:33/0  |  提交时间:2023/12/27
Acoustic multi-parameter full waveform inversion based on the wavelet method 期刊论文  OAI收割
INVERSE PROBLEMS IN SCIENCE AND ENGINEERING, 2020, 页码: 28
作者:  
Zhang, Wensheng
  |  收藏  |  浏览/下载:20/0  |  提交时间:2020/09/23
UAV target saliency detection based on frequency domain transform 会议论文  OAI收割
Xiamen, PEOPLES R CHINA, 2020-08-25
作者:  
Wang, Xin;  Li, Zhe;  Tian, Yan
  |  收藏  |  浏览/下载:22/0  |  提交时间:2021/06/04
A Reweighted Joint Spatial-Radon Domain CT Image Reconstruction Model for Metal Artifact Reduction 期刊论文  OAI收割
SIAM JOURNAL ON IMAGING SCIENCES, 2018, 卷号: 11, 期号: 1, 页码: 707-733
作者:  
Zhang, HM;  Liu, BD;  Liu BD(刘宝东);  Dong, B
  |  收藏  |  浏览/下载:33/0  |  提交时间:2019/09/24
A reweighted joint spatial-radon domain ct image reconstruction model for metal artifact reduction 期刊论文  iSwitch采集
Siam journal on imaging sciences, 2018, 卷号: 11, 期号: 1, 页码: 707-733
作者:  
Zhang, Haimiao;  Dong, Bin;  Liu, Baodong
收藏  |  浏览/下载:55/0  |  提交时间:2019/04/23
Research of OBC and cable data matched filtering in wavelet domain 期刊论文  OAI收割
CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION, 2014, 卷号: 57, 期号: 1, 页码: x
Zhu, JM; Fang, ZY; Zhang, XY; Zhang, LX; Xie, SL
收藏  |  浏览/下载:34/0  |  提交时间:2014/12/11
Frequency-related factors analysis in frequency domain waveform inversion 期刊论文  OAI收割
CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION, 2012, 卷号: 55, 期号: 4, 页码: 1345-1353
作者:  
Liu Guo-Feng;  Liu Hong;  Meng Xiao-Hong;  Yan Hao-Fei
  |  收藏  |  浏览/下载:21/0  |  提交时间:2018/09/26
Frequency-related factors analysis in frequency domain waveform inversion 期刊论文  OAI收割
CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION, 2012, 卷号: 55, 期号: 4, 页码: 1345-1353
作者:  
Liu Guo-Feng;  Liu Hong;  Meng Xiao-Hong;  Yan Hao-Fei
  |  收藏  |  浏览/下载:16/0  |  提交时间:2018/09/26
Phase-locked and non-phase-locked event-related oscillations and channel power spectra analysis during motor imagery with speed parameters for BCRI 会议论文  OAI收割
5th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2011, Wuhan, China, May 10-12, 2011
作者:  
Fu YF(伏云发);  Xu BL(徐保磊);  Pei LL(裴立力);  Li HY(李洪谊)
收藏  |  浏览/下载:42/0  |  提交时间:2012/06/06
Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform (EI CONFERENCE) 会议论文  OAI收割
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications, May 24, 2011 - May 26, 2011, Beijing, China
Wu Z.-G.; Wang M.-J.; Han G.-L.
收藏  |  浏览/下载:84/0  |  提交时间:2013/03/25
Being an efficient method of information fusion  image fusion has been used in many fields such as machine vision  medical diagnosis  military applications and remote sensing.In this paper  Pulse Coupled Neural Network (PCNN) is introduced in this research field for its interesting properties in image processing  including segmentation  target recognition et al.  and a novel algorithm based on PCNN and Wavelet Transform for Multi-focus image fusion is proposed. First  the two original images are decomposed by wavelet transform. Then  based on the PCNN  a fusion rule in the Wavelet domain is given. This algorithm uses the wavelet coefficient in each frequency domain as the linking strength  so that its value can be chosen adaptively. Wavelet coefficients map to the range of image gray-scale. The output threshold function attenuates to minimum gray over time. Then all pixels of image get the ignition. So  the output of PCNN in each iteration time is ignition wavelet coefficients of threshold strength in different time. At this moment  the sequences of ignition of wavelet coefficients represent ignition timing of each neuron. The ignition timing of PCNN in each neuron is mapped to corresponding image gray-scale range  which is a picture of ignition timing mapping. Then it can judge the targets in the neuron are obvious features or not obvious. The fusion coefficients are decided by the compare-selection operator with the firing time gradient maps and the fusion image is reconstructed by wavelet inverse transform. Furthermore  by this algorithm  the threshold adjusting constant is estimated by appointed iteration number. Furthermore  In order to sufficient reflect order of the firing time  the threshold adjusting constant is estimated by appointed iteration number. So after the iteration achieved  each of the wavelet coefficient is activated. In order to verify the effectiveness of proposed rules  the experiments upon Multi-focus image are done. Moreover  comparative results of evaluating fusion quality are listed. The experimental results show that the method can effectively enhance the edge details and improve the spatial resolution of the image. 2011 SPIE.