中国科学院机构知识库网格系统: 检索

p A Mixed Wavelet-Learning Method of Predicting Macroscopic Effective Heat Transfer Conductivities of Braided Composite Materials 期刊论文 OAI收割
COMMUNICATIONS IN COMPUTATIONAL PHYSICS, 2022, 卷号: 31, 期号: 2, 页码: 593-625
作者: Dong, Hao

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收藏 | 浏览/下载：35/0 | 提交时间：2022/04/02

Braided composite materials macroscopic effective heat transfer conductivities multi-scale modeling neural networks wavelet transform

Multi-source Remote Sensing Image Registration Based on Contourlet Transform and Multiple Feature Fusion 期刊论文 OAI收割
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 5, 页码: 575-588
作者: Huan Liu; Gen-Fu Xiao; Yun-Lan Tan; Chun-Juan Ouyang

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收藏 | 浏览/下载：7/0 | 提交时间：2021/02/22

Feature fusion multi-scale circle Gaussian combined invariant moment multi-direction gray level co-occurrence matrix multi-source remote sensing image registration contourlet transform.

A new image denoising method based on wavelet multi-scale registration fusion 会议论文 OAI收割
Shenzhen, China, July 13-15, 2018
作者: Ma Y(马钺); Gao L(高亮); Wu, Jing Hui; Chen S(陈帅); Wu JH(吴景辉)

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收藏 | 浏览/下载：29/0 | 提交时间：2019/03/09

image denoising wavelet transform wavelet multi-scale registration fusion improved wavelet threshold shrink

Multi-Scale Blobs for Saliency Detection in Satellite Images SCI/SSCI论文 OAI收割
2016
作者: Zhou Y. N.; Luo, J. C.; Hu, X. D.; Shen, Z. F.; Yu, GR

收藏 | 浏览/下载：26/0 | 提交时间：2016/12/16

Multi-scale blob Multi-level distance transform Salient region Object center Satellite image remote-sensing images features scale

Experimental study on spectrum and multi-scale nature of wall pressure and velocity in turbulent boundary layer 期刊论文 OAI收割
CHINESE PHYSICS B, 2015, 卷号: 24, 期号: 6, 页码: 64702
作者: Zheng Xiao-Bo; Jiang Nan

收藏 | 浏览/下载：56/0 | 提交时间：2015/09/22

multi-scale coherent structures hot wire anemometry microphone wavelet transform

Improved continuous wavelet analysis of variation in the dominant period of hydrological time series SCI/SSCI论文 OAI收割
2013
作者: Wang D.

收藏 | 浏览/下载：15/0 | 提交时间：2014/12/24

hydrological series analysis continuous wavelet transform wavelet basis function multi-temporal scale wavelet spectrum period trend change points maximum entropy spectral analysis de-noising entropy spectral-analysis decomposition identification transform threshold model

A fast target recognition algorithm based on MSA and MSR (EI CONFERENCE) 会议论文 OAI收割
2012 International Conference on Industrial Control and Electronics Engineering, ICICEE 2012, August 23, 2012 - August 25, 2012, Xi'an, China
作者: Wang Y.; Liu G.; Wang Y.; Wang Y.; Wang Y.

收藏 | 浏览/下载：31/0 | 提交时间：2013/03/25

This paper presents a new fast target recognition algorithm the proposed method is based on Multi-scale Auto convolution(MSA) and Multi-scale Retinex(MSR). As shown by the comparison with original MSA it appears that this new technique solves the problem that MSA algorithm is sensitive to illumination and the computational load is significantly reduced to 1/8th of that of the original MSA algorithm it is also robust to affine transform light projective transform noise thin fog occlusion and illumination change. the performed experiments show that it has fast searching speed and can accurately recognize and locate target in real scenes. 2012 IEEE.

Image coding using wavelet-based compressive sampling (EI CONFERENCE) 会议论文 OAI收割
2012 5th International Symposium on Computational Intelligence and Design, ISCID 2012, October 28, 2012 - October 29, 2012, Hangzhou, China
作者: Li J.; Li J.; Li J.

收藏 | 浏览/下载：45/0 | 提交时间：2013/03/25

In this paper we proposed a novel coding scheme is proposed using wavelet-based CS framework for nature image. First two-dimension discrete wavelet transform (DWT) is applied to a nature image for sparse representation. After multi-scale DWT the low-frequency sub-band and high-frequency sub-bands are re-sampled separately. According to the statistical dependences among DWT coefficients we allocate different measurements to low- and high-frequency component. Then the measurements samples can be quantized. The quantize samples are entropy coded and forward correct coding (FEC). Finally the compressed streams are transmitted. At the decoder one can simply reconstruct the image via l1 minimization. Experimental results show that the proposed wavelet-based CS scheme achieves better compression performance against the relevant existing solutions.

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：78/0 | 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.

Improved Fusion Method for Infrared and Visible Remote Sensing Imagery Using NSCT 会议论文 OAI收割
2011 6th Ieee Conference on Industrial Electronics and Applications, New York
Huang Qingqing; Ji Yuan; Yang Jian

收藏 | 浏览/下载：23/0 | 提交时间：2014/12/07

Image fusion Multi-scale Transform Discrete Wavelet Transform Region energy Nonsubsampled Contourlet transform(NSCT) CONTOURLET TRANSFORM