EEG epileptic seizure detection and classification based on dual-tree complex wavelet transform and machine learning algorithms

The visual analysis of common neurological disorders such as epileptic seizures in electroencephalography(EEG) is an oversensitive operation and prone to errors,which has motivated the researchers to develop effective automated seizure detection methods.This paper proposes a robust automatic seizure detection method that can establish a veritable diagnosis of these diseases.The proposed method consists of three steps:(i) remove artifact from EEG data using Savitzky-Golay filter and multi-scale principal component analysis(MSPCA),(ii) extract features from EEG signals using signal decomposition representations based on empirical mode decomposition(EMD),discrete wavelet transform(DWT),and dual-tree complex wavelet transform(DTCWT) allowing to overcome the non-linearity and non-stationary of EEG signals,and(iii) allocate the feature vector to the relevant class(i.e.,seizure class "ictal" or free seizure class "interictal") using machine learning techniques such as support vector machine(SVM),k-nearest neighbor(k-NN),and linear discriminant analysis(LDA).The experimental results were based on two EEG datasets generated from the CHB-MIT database with and without overlapping process.The results obtained have shown the effectiveness of the proposed method that allows achieving a higher classification accuracy rate up to 100% and also outperforms similar state-of-the-art methods.

Image inpainting using complex 2-D dual-tree wavelet transform

The dual-tree complex wavelet transform is a useful tool in signal and image process- ing. In this paper, we propose a dual-tree complex wavelet transform （CWT） based algorithm for image inpalnting problem. Our approach is based on Cai, Chan, Shen and Shen＇s framelet-based algorithm. The complex wavelet transform outperforms the standard real wavelet transform in the sense of shift-invariance, directionality and anti-aliasing. Numerical results illustrate the good performance of our algorithm.

Seismic signal analysis based on the dual-tree complex wavelet packet transform

We tried to apply the dual-tree complex wavelet packet transform in seismic signal analysis. The complex wavelet packet transform (CWPT) combine the merits of real wavelet packet transform with that of complex continuous wavelet transform (CCWT). It can not only pick up the phase information of signal, but also produce better ″focal- izing″ function if it matches the phase spectrum of signals analyzed. We here described the dual-tree CWPT algo- rithm, and gave the examples of simulation and actual seismic signals analysis. As shown by our results, the dual-tree CWPT is a very effective method in analyzing seismic signals with non-linear phase.

Defects Recognition of 3D Braided Composite Based on Dual-Tree Complex Wavelet Packet Transform

Textile-reinforced composites,due to their excellent highstrength-to-low-mass ratio, provide promising alternatives to conventional structural materials in many high-tech sectors. 3D braided composites are a kind of advanced composites reinforced with 3D braided fabrics; the complex nature of 3D braided composites makes the evaluation of the quality of the product very difficult. In this investigation,a defect recognition platform for 3D braided composites evaluation was constructed based on dual-tree complex wavelet packet transform( DT-CWPT) and backpropagation( BP) neural networks. The defects in 3D braided composite materials were probed and detected by an ultrasonic sensing system. DT-CWPT method was used to analyze the ultrasonic scanning pulse signals,and the feature vectors of these signals were extracted into the BP neural networks as samples. The type of defects was identified and recognized with the characteristic ultrasonic wave spectra. The position of defects for the test samples can be determined at the same time. This method would have great potential to evaluate the quality of 3D braided composites.

基于DTCWT-VAE的弹道中段目标RCS识别

针对弹道目标雷达信号易受环境影响、目标识别准确率低的问题,提出了一种基于双树复小波变换(dual-tree complex wavelet transform,DTCWT)和变分自编码器(variational autoencoder,VAE)的弹道目标雷达散射截面(radar cross section,RCS)识别法。首先,采用DTCWT对弹道目标RCS动态数据进行预处理,再利用VAE提取目标的隐变量特征,最后用支持向量机(support vector machine,SVM)分类器进行识别。实验结果表明,与已有方法相比,该方法具有更高的识别概率,且鲁棒性较好。

NEW METHOD OF EXTRACTING WEAK FAILURE INFORMATION IN GEARBOX BY COMPLEX WAVELET DENOISING

Because the extract of the weak failure information is always the difficulty and focus of fault detection. Aiming for specific statistical properties of complex wavelet coefficients of gearbox vibration signals, a new signal-denoising method which uses local adaptive algorithm based on dual-tree complex wavelet transform （DT-CWT） is introduced to extract weak failure information in gear, especially to extract impulse components. By taking into account the non-Gaussian probability distribution and the statistical dependencies among wavelet coefficients of some signals, and by taking the advantage of near shift-invariance of DT-CWT, the higher signal-to-noise ratio （SNR） than common wavelet denoising methods can be obtained. Experiments of extracting periodic impulses in gearbox vibration signals indicate that the method can extract incipient fault feature and hidden information from heavy noise, and it has an excellent effect on identifying weak feature signals in gearbox vibration signals.

Recognition of Group Activities Using Complex Wavelet Domain Based Cayley-Klein Metric Learning

A group activity recognition algorithm is proposed to improve the recognition accuracy in video surveillance by using complex wavelet domain based Cayley-Klein metric learning.Non-sampled dual-tree complex wavelet packet transform(NS-DTCWPT)is used to decompose the human images in videos into multi-scale and multi-resolution.An improved local binary pattern(ILBP)and an inner-distance shape context(IDSC)combined with bag-of-words model is adopted to extract the decomposed high and low frequency coefficient features.The extracted coefficient features of the training samples are used to optimize Cayley-Klein metric matrix by solving a nonlinear optimization problem.The group activities in videos are recognized by using the method of feature extraction and Cayley-Klein metric learning.Experimental results on behave video set,group activity video set,and self-built video set show that the proposed algorithm has higher recognition accuracy than the existing algorithms.

Low-light image enhancement based on Retinex theory and dual-tree complex wavelet transform

In order to enhance the contrast of low-light images and reduce noise in them, we propose an image enhancement method based on Retinex theory and dual-tree complex wavelet transform(DT-CWT). The method first converts an image from the RGB color space to the HSV color space and decomposes the V-channel by dual-tree complex wavelet transform. Next, an improved local adaptive tone mapping method is applied to process the low frequency components of the image, and a soft threshold denoising algorithm is used to denoise the high frequency components of the image. Then, the V-channel is rebuilt and the contrast is adjusted using white balance method. Finally, the processed image is converted back into the RGB color space as the enhanced result. Experimental results show that the proposed method can effectively improve the performance in terms of contrast enhancement, noise reduction and color reproduction.

A Dual-Tree Complex Wavelet Transform-Based Model for Low-Illumination Image Enhancement

Image enhancement is a monumental task in the field of computer vision and image processing.Existing methods are insufficient for preserving naturalness and minimizing noise in images.This article discusses a technique that is based on wavelets for optimizing images taken in low-light.First,the V channel is created by mapping an image’s RGB channel to the HSV color space.Second,the acquired V channel is decomposed using the dual-tree complex wavelet transform(DT-CWT)in order to recover the concentrated information within its high and low-frequency subbands.Thirdly,an adaptive illumination boost technique is used to enhance the visibility of a low-frequency component.Simultaneously,anisotropic diffusion is used to mitigate the high-frequency component’s noise impact.To improve the results,the image is reconstructed using an inverse DT-CWT and then converted to RGB space using the newly calculated V.Additionally,images are white-balanced to remove color casts.Experiments demonstrate that the proposed approach significantly improves outcomes and outperforms previously reported methods in general.

基于颜色直方图及双树复小波变换(DTCWT)的昆虫图像识别

为了给生产单位害虫管理的普通技术人员提供简便易操作的昆虫鉴别方法,本文提出了一种新颖的基于图像颜色及纹理特征的昆虫图像识别方法。鳞翅目昆虫翅面图像经过预处理,确定目标区域,再进行特征提取。首先将彩色图像从三原色(red-green-blue,RGB)空间转换至色调饱和值(HSV)空间并提取有效区域内的色度、饱和度直方图特征,然后经图像位置校准,提取灰度图的双树复小波变换(DTCWT)特征;匹配首先计算两颜色直方图特征向量之间的相关性,将相关性大于阈值的样本再进一步用DTCWT特征匹配;DTCWT匹配通过计算Canberra距离实现,从通过第一层颜色匹配的样本中取出最近邻作为最终匹配类别。算法在包含100类鳞翅目昆虫的图像库中进行试验验证,取得了76%的识别率,其中前翅识别率则达92%,同时取得了理想的时间性能。试验结果证明了本文方法的有效性。

基于DTCWT和LBP的低分辨率人脸识别

针对短时傅里叶变换频率分辨率较差的缺点,提出一种基于双树复小波变换(DTCWT)和局部二进制模式(LBP)直方图的低分辨率人脸识别方法。使用DTCWT获得人脸图像的多尺度多方向的频率幅度响应,采用LBP获取频率幅度响应的统计直方图,通过基于统计的一致性模式得到更加紧凑的统计分布特征。实验结果表明,该方法在低分辨率人脸上可以达到较高的识别准确率。

基于稀疏去噪的DTCWT火焰图像融合检测

燃煤火焰图像黑把子区域的边缘模糊或不完整,无法直接使用Canny检测算子准确检测出边缘信息。针对该问题,提出基于稀疏去噪的双树复小波变换(DTCWT)火焰图像融合检测方法。利用稀疏去噪对2幅单帧火焰图像进行DTCWT融合,采用Canny检测算子检测边缘。实验结果表明,该方法能够得到噪声较低的图像和比较完整的黑把子边缘信息。

基于M-DTCWT和2APCNN的多聚焦图像融合

为提高多聚焦图像的融合质量,提出了一种基于多方向双树复小波变换(M-DTCWT)的多聚焦图像融合方法。对多聚焦图像进行DTCWT分解得到低频系数与高频系数,再采用非下采样滤波器(NSDFB)对高频系数进行方向分解得到多尺度多方向的高频分解系数。对低频系数,提出结合模糊逻辑和稀疏表示(FSR)的融合规则得到低频融合系数。对高频系数,利用平均高斯差分梯度(ADOG)作为自适应双通道脉冲耦合神经网络(2APCNN)链接强度,提出基于改进双通道脉冲耦合神经网络的高频融合策略。最后通过M-DTCWT的反变换得到融合图像。实验结果表明,采用本文算法得到的融合图像在主观效果与客观指标上均优于传统的融合方法,较传统DTCWT方法,实验的2组图像在客观指标边缘信息度量Q AB/F和互信息MI上,分别提高了1.93%、8.87%和1.40%、9.18%。

Insect recognition based on integrated region matching and dual tree complex wavelet transform

To provide pest technicians with a convenient way to recognize insects,a novel method is proposed to classify insect images by integrated region matching (IRM) and dual tree complex wavelet transform (DTCWT).The wing image of the lepidopteran insect is preprocessed to obtain the region of interest (ROI) whose position is then calibrated.The ROI is first segmented with the k-means algorithm into regions according to the color features,properties of all the segmented regions being used as a coarse level feature.The color image is then converted to a grayscale image,where DTCWT features are extracted as a fine level feature.The IRM scheme is undertaken to find K nearest neighbors (KNNs),out of which the nearest neighbor is searched by computing the Canberra distance of DTCWT features.The method was tested with a database including 100 lepidopteran insect species from 18 families and the recognition accuracy was 84.47%.For the forewing subset,a recognition accuracy of 92.38% was achieved.The results showed that the proposed method can effectively solve the problem of automatic species identification of lepidopteran specimens.

基于双树复小波和深度信念网络的轴承故障诊断

提出了一种基于双树复小波(DTCWT)和深度信念网络(DBN)的轴承故障诊断新方法。采用DTCWT对轴承振动信号进行分解实验,结果表明DTCWT能够很好地将信号分解到不同频带。进而提取能量熵作为故障特征,采用DBN小样本分类模型对轴承故障进行分类,并与传统分类器进行比较,结果表明该方法能准确识别不同故障类型,扩展了DBN在机械故障诊断中的应用。

基于局部熵的量子衍生医学超声图像去斑

针对现有医学超声图像去斑方法的不足,该文提出一种基于局部熵的量子衍生医学超声图像去斑新方法。首先,将对数变换后的图像进行双树复小波变换(DTCWT),并对信号与噪声分别建模;然后,提取复小波中子代与父代小波系数的实部,计算其局部熵并进行归一化乘积,结合量子衍生理论得到用来调整信号与噪声出现概率的可调参数;最后,利用改进的双变量收缩函数获得去斑后的图像。通过实验,结果表明该方法与已有方法相比能够更有效地滤除医学超声图像中的斑点噪声并保留细节信息。

基于双树复小波和自适应权重和时间因子的粒子群优化支持向量机的轴承故障诊断

提出了一种基于双树复小波和具有自适应权重和时间因子的粒子群算法优化支持向量机的轴承故障诊断方法。首先对机械振动信号进行DTCWT变换,提取能量熵作为特征向量。然后采用AWTFPSO算法优化SVM,实现轴承故障诊断。不同方法的对比实验及分析结果表明,该方法速度快、准确率高。

双密度双树复小波变换的局域自适应图像去噪

为了改善降质图像质量,提出一种基于双密度双树复小波变换的局域自适应图像去噪算法。分析了双密度双树复小波变换的原理及特点,推导了双变量收缩函数(BSF)。通过并行使用4个2D双密度离散小波变换,且行和列采用不同的滤波器组,实现了对噪声图像的双密度双树复小波分解。根据小波系数的统计特性以及层内和层间系数的相关性,采用结合局域方差估计的双变量收缩函数对小波系数进行处理,并用收缩后的小波系数重构去噪图像。最后,将该算法用于灰度图像和彩色图像去噪实验。实验结果表明:与噪声图像相比,在噪声方差为30时,经该算法去噪后的图像获得的最高峰值信噪比增益达11.72 dB,平均结构相似度最高增加了2.7倍,复合峰值信噪比增益达11.68 dB。此外,对不同噪声方差下的不同噪声图像,该算法在滤除噪声的同时可保留更多的图像细节,极大地改善了去噪图像的视觉质量。

基于双树复小波和奇异差分谱的齿轮故障诊断研究

针对齿轮故障振动信号的非平稳特性和包含强烈噪声,很难提取故障特征频率的情况,提出了基于双树复小波和奇异差分谱的故障诊断方法。首先将非平稳的故障振动信号通过双树复小波分解为几个不同频段的分量;由于噪声的影响,从各个分量的频谱中难以准确地得到故障频率。然后对包含故障特征的分量构建Hankel矩阵并进行奇异值分解,求奇异值差分谱曲线,确定奇异值个数进行SVD重构降噪,由此实现对故障特征信息的提取。最后再求希尔伯特包络谱,便能准确地得到故障频率。实验结果和工程应用表明,该方法可以有效地提取齿轮的故障特征信息,验证了方法的可行性和有效性。

基于双树复数小波的结构相似度测量法

以人类视觉系统更符合提取图像结构信息为前提的结构相似度评价模型,因其高效特性受到广泛重视,但与传统图像质量评价方法一样,其性能易受比较图像几何变化的影响.为了有效地评价各类图像质量,提出了一种双树复数小波与结构相似性测量相结合的全参考客观评价方法.基于双树复数小波的平移不变性、方向选择性和低冗余特性,首先对图像进行双树复数小波变换,并在复数域计算结构相似度,然后经平均获得客观质量评价分,改善了对几何失真图像的评价性能,且实现简单、计算量小.在LIVE图库上的实验结果表明,提出的模型对亮度、对比度以及几何变化具备更强的稳定性.