中国科学院机构知识库网格系统: 检索

Method for early diagnosis of verticillium wilt in cotton based on chlorophyll fluorescence and hyperspectral technology 期刊论文 OAI收割
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2024, 卷号: 216, 页码: 12
作者: Yang, Mi; Kang, Xiaoyan; Qiu, Xiaofeng; Ma, Lulu; Ren, Hong

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收藏 | 浏览/下载：58/0 | 提交时间：2024/03/25

Wavelet transform Features selection Microstructure Early detection of disease

Dynamical Conventional Neural Network Channel Pruning by Genetic Wavelet Channel Search for Image Classification 期刊论文 OAI收割
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE, 2021, 卷号: 15, 页码: 11
作者: Chen, Lin; Gong, Saijun; Shi, Xiaoyu; Shang, Mingsheng

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收藏 | 浏览/下载：45/0 | 提交时间：2021/12/28

neural network pruning neural architecture search wavelet features neural network compression image classification

A(3)GAN: An Attribute-Aware Attentive Generative Adversarial Network for Face Aging 期刊论文 OAI收割
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2021, 卷号: 16, 页码: 2776-2790
作者: Liu, Yunfan; Li, Qi; Sun, Zhenan; Tan, Tieniu

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收藏 | 浏览/下载：36/0 | 提交时间：2021/05/31

Aging Faces Face recognition Facial features Generators Wavelet packets Visualization Generative adversarial networks face aging facial attribute attention mechanism wavelet packet transform

Global and Local Consistent Wavelet-Domain Age Synthesis 期刊论文 OAI收割
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2019, 卷号: 14, 期号: 11, 页码: 2943-2957
作者: Li, Peipei; Hu, Yibo; He, Ran; Sun, Zhenan

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收藏 | 浏览/下载：78/0 | 提交时间：2019/12/16

Age synthesis wavelet transform generative adversarial network global and local features

A novel bearing fault diagnosis method based on principal component analysis and BP neural network 会议论文 OAI收割
Changsha, China, November 1-3, 2019
作者: Sun Y(孙越); Xu AD(徐皑冬); Wang K(王锴); Han XJ(韩晓佳); Guo HF(郭海丰)

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收藏 | 浏览/下载：25/0 | 提交时间：2020/06/21

Fault diagnosis rolling bearing principal component analysis wavelet packet energy high dimensional features

Multi-scale decomposition of point process data SCI/SSCI论文 OAI收割
2012
作者: Ma T.; Pei T.

收藏 | 浏览/下载：48/0 | 提交时间：2014/12/24

Density based clustering method Homogeneous point process Wavelet transform MCMC EM kth nearest distance nearest-neighbor method delaunay triangulation density-estimation genetic algorithm spatial-patterns cluster-analysis model features inference space

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.

Detection of low contrast targets based on lifting scheme wavelet transform (EI CONFERENCE) 会议论文 OAI收割
2009 IEEE International Conference on Mechatronics and Automation, ICMA 2009, August 9, 2009 - August 12, 2009, Changchun, China
作者: Chen X.; Wang Y.; Wang Y.; Wang Y.; Wang Y.

收藏 | 浏览/下载：18/0 | 提交时间：2013/03/25

This paper present a fast algorithm for detection of low contrast objects by using wavelet filters based on lifting scheme. The advantage is robust to noise. According Swelden's lifting wavelet filters are biorthogonal wavelet filters containing free parameters. We use reference image of targets to train the lifting terms so that the learnt wavelet filters have the features of targets. Then applying such filters to the images including targets taken from camera system. We can detect the locations where the high frequency components are almost the same as those of the target image. 2009 IEEE.

AN IMPROVED FUSION METHOD FOR PAN-SHARPENING BEIJING-1 MICRO-SATELLITE IMAGES 会议论文 OAI收割
2009 Ieee International Geoscience and Remote Sensing Symposium, Vols 1-5, New York
Liu, Haixia; Zhang, Bing; Zhang, Xia; Li, Junsheng; Chen, Zhengchao; Zhou, Xiaoxue

收藏 | 浏览/下载：27/0 | 提交时间：2014/12/07

Fusion Beijing-1 Micro-Satellite IHS-Wavelet Weighted regional features

CR image filter methods research based on wavelet-domain hidden markov models (EI CONFERENCE) 会议论文 OAI收割
ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China
作者: Wang J.-L.; Wang J.-L.; Li D.-Y.; Wang Y.-P.

收藏 | 浏览/下载：23/0 | 提交时间：2013/03/25

In the procedure of computed radiography imaging we should firstly get across the characters of kinds of noises and the relationship between the image signals and noises. Based on the specialties of computed radiography (CR) images and medical image processing we have study the filtering methods for computed radiography images noises. On the base of analyzing computed radiography imaging system in detail the author think that the major two noises are Gaussian white noise and Poisson noise. Then the different relationship of between two kinds of noises and signal were studied completely. By considering both the characteristics of computed radiography images and the statistical features of wavelet transformed images a multiscale image filtering algorithm which based on two-state hidden markov model (HMM) and mixture Gaussian statistical model has been used to decrease the Gaussian white noise in computed images. By using EM (Expectation Maximization) algorithm to estimate noise coefficients in each scale and obtain power spectrum matrix then this carried through the syncretized two Filter that are IIR(infinite impulse response) Wiener Filter and HMM according to scale size and achieve the experiments as well as the comparison with other denoising methods were presented at last.