新的纹理图像特征提取方法

提出了一种新的基于非下采样Contourlet变换的纹理特征提取方法.首先对纹理图像进行非下采样Contourlet变换,然后提取不同尺度、不同方向上变换系数矩阵的均值和方差作为特征向量,大大降低了特征维数,并利用BP神经网络进行训练和仿真,实现了纹理图像的自动分类.实验结果表明,与小波包变换和改进的LBP纹理算子等方法相比,该方法能取得更好的分类效果.     

基于多尺度小波包分析的肺音特征提取与分类

提出了一种适于非平稳肺音信号的特征提取方法.以4种肺音信号（正常、气管炎、肺炎和哮喘）为样本数据,通过分析肺音信号的时频分布特点,选择了具有任意多分辨分解特性的小波包.对小波包进行空间划分后找到了适合肺音特征提取的最优基,并基于最优基对肺音信号进行快速多尺度的分解,得到了各级节点的高维小波系数矩阵,建立了小波系数与信号能量在时域上的等价关系,并将能量作为特征值,构造了低维的作为分类神经网络的输入特征矢量,大大降低了输入特征的维数.研究表明该算法的识别性能是高效的.     

一种基于三进制小波变换的纹理分割方法

提出了一种基于三进制小波变换的纹理特征提取方法，多纹理图像经过三进制小波滤波器组后得到九个子带图像，采用小波系数的标准差作为纹理测度以生成特征向量，利用常规的C-均值聚类算法进行纹理分割，实验结果表明了该特征提取方法的有效性。     

基于小波和SOM网络的医学图像融合

提出一种基于小波变换和自组织特征映射（SOM）神经网络的医学图像融合方法,对图像进行小波变换,以图像的小波系数为特征,采用SOM网络对图像进行聚类,并进行模糊分类,从而确定像素融合的权重,得到融合图像。仿真实验结果表明,该方法能够获得良好的性能。     

改进的模糊聚类在控制系统故障诊断中的应用

为了提高控制系统中传感器与执行器故障诊断的准确性,结合小波分析特征提取的优势和密度函数加权模糊C-均值聚类具有较好分类效果的特点,提出了一种新的控制系统故障诊断方法。该方法首先利用小波分析对故障信号进行特征提取,降低噪声的影响;然后对特征提取后的数据通过加权模糊C-均值聚类算法,对故障进行识别分类。实验表明,基于小波分析和加权模糊C-均值聚类相结合的方法,不仅可以识别不同部件的故障,而且可以对同一部件的不同类型的故障进行诊断。     

一种基于纹理的图像分割方法

提出了一种基于多进制小波变换的纹理特征提取方法，通过对小波系数的标准差作为纹理测度以生成特征向量，利用模糊c-均值聚类算法进行纹理分割，获得了较好实验结果。     

雷达辐射源信号小波变换特征提取方法

在小波域滤波算法的基础上提出一种对雷达辐射源信号进行脉内特征提取方法,该方法能够从信号中有效地提取定量信息。将小波变换后低频逼近小波系数的能量分布熵与经过尺度相关去噪计算后反映信号边缘的高频细节小波系数能量分布熵构成雷达辐射源信号的二维特征向量。通过对10种雷达辐射源信号的特征提取和分类仿真实验分析表明：提取的样本特征在0 dB下具有很好的抗噪性和可聚类性,方法是有效的。该方法能够简化分类器的设计,有利于工程应用。     

利用多进制小波变换和模糊c-均值聚类进行纹理分割

提出了一种基于多进制小波变换的多纹理特征提取方法,通过对小波系数的标准差作为纹理测度以生成特征向量,利用模糊c-均值聚类算法进行纹理分割,获得了较好的实验结果。     

基于模糊聚类的小波变换图像去噪算法改进

介绍一种改进的较优的基于模糊聚类的小波变换图像去噪算法.首先分析了模糊C均值聚类算法中加权指数m的重要性,采用基于模糊决策的方法,分别构造模糊目标和模糊约束,由模糊目标和模糊约束的交集来共同确定最优的加权指数m以获取较为理想的聚类分类结果.再利用该种加权模糊聚类算法把小波系数划分成包含信号与只包含噪声的小波系数两类,将只包含噪声的小波系数置为零,将包含信号的小波系数利用软阈值法进行收缩,最后对处理后的系数根据M带小波变换的局部时频分析能力及其良好的信噪分离能力进行M带小波变换,得到去噪效果较好的图像.     

基于无抽样小波变换和MCE训练的纹理分类

提出了一种新的纹理分类的方法,该方法把基于无抽样小波变换的特征提取器和基于欧几里得距离的分类器进行了合并。把方差、偏态系数、峰态系数、三者的联合及谱直方图作为描述纹理图像不相重叠的图像窗的特征。一个使用线性转换矩阵的特征提取器对分类导向的特征做进一步的提取。利用基于欧几里得距离的分类器,每个纹理图像不相重叠的图像窗被确定到属于它的那一类。基于最小分类错误训练方法的特征提取器和分类器设计的合并使分类错误达到了最小化。使用该方法对25类BrodTex纹理图像进行了评估,分类精确度达到90%以上。     

Feature extraction from wavelet coefficients for patternrecognition tasks

An efficient feature extraction method based on the fast wavelet transform is presented. The paper especially deals with the assessment of process parameters or states in a given application using the features extracted from the wavelet coefficients of measured process signals. Since the parameter assessment using all wavelet coefficients will often turn out to be tedious or leads to inaccurate results, a preprocessing routine that computes robust features correlated to the process parameters of interest is highly desirable. The method presented divides the matrix of computed wavelet coefficients into clusters equal to row vectors. The rows that represent important frequency ranges (for signal interpretation) have a larger number of clusters than the rows that represent less important frequency ranges. The features of a process signal are eventually calculated by the Euclidean norms of the clusters. The effectiveness of this new method has been verified on a flank wear estimation problem in turning processes and on a problem of recognizing different kinds of lung sounds for diagnosis of pulmonary diseases     

A cluster-based wavelet feature extraction method and its application

In this paper, a new cluster-based approach is proposed for extracting features from the coefficients of a two-dimensional discrete wavelet transform. The wavelet coefficients from the matrix of each frequency channel are segregated into non-overlapping clusters in an unsupervised mode using a set of application-specific representative images. In practical situations, this set of representative images can be the same as the ones kept aside for training a classifier. The proposed method divides the matrices of computed wavelet coefficients into disjoint clusters that are centered around the position of dominant coefficients. The features that can distinguish images of one class from those of other classes are obtained by computing energies of the clusters. The feature vectors so obtained are then presented as input patterns to an image classifier, such as a neural network. Experimental results based on the applications for texture classification and wood surface defect detection have shown that the proposed cluster-based wavelet feature extraction method is able to effectively extract important intrinsic information content from the test images, and increase the overall classification accuracy as compared with conventional feature extraction methods.     

Probabilistic neural networks combined with wavelet coefficients for analysis of electroencephalogram signals

Abstract: In this paper, the probabilistic neural network is presented for classification of electroencephalogram (EEG) signals. Decision making is performed in two stages: feature extraction by wavelet transform and classification using the classifiers trained on the extracted features. The purpose is to determine an optimum classification scheme for this problem and also to infer clues about the extracted features. The present research demonstrates that the wavelet coefficients obtained by the wavelet transform are features which represent the EEG signals well. The conclusions indicate that the probabilistic neural network trained on the wavelet coefficients achieves high classification accuracies (the total classification accuracy is 97.63%).     

A new optimum feature extraction and classification method for speaker recognition: GWPNN

Speech and speaker recognition is an important topic to be performed by a computer system. In this paper, an expert speaker recognition system based on optimum wavelet packet entropy is proposed for speaker recognition by using real speech/voice signal. This study contains both the combination of the new feature extraction and classification approach by using optimum wavelet packet entropy parameter values. These optimum wavelet packet entropy values are obtained from measured real English language speech/voice signal waveforms using speech experimental set. A genetic-wavelet packet-neural network (GWPNN) model is developed in this study. GWPNN includes three layers which are genetic algorithm, wavelet packet and multi-layer perception. The genetic algorithm layer of GWPNN is used for selecting the feature extraction method and obtaining the optimum wavelet entropy parameter values. In this study, one of the four different feature extraction methods is selected by using genetic algorithm. Alternative feature extraction methods are wavelet packet decomposition, wavelet packet decomposition – short-time Fourier transform, wavelet packet decomposition – Born–Jordan time–frequency representation, wavelet packet decomposition – Choi–Williams time–frequency representation. The wavelet packet layer is used for optimum feature extraction in the time–frequency domain and is composed of wavelet packet decomposition and wavelet packet entropies. The multi-layer perceptron of GWPNN, which is a feed-forward neural network, is used for evaluating the fitness function of the genetic algorithm and for classification speakers. The performance of the developed system has been evaluated by using noisy English speech/voice signals. The test results showed that this system was effective in detecting real speech signals. The correct classification rate was about 85% for speaker classification.     

A wavelet neural network for the detection of heart valve diseases

An expert system is presented for interpretation of the Doppler signals of heart valve diseases based on pattern recognition. We deal in particular with the combination of feature extraction and classification from measured Doppler signal waveforms at the heart valve using Doppler ultrasound. A wavelet neural network model developed by us is used. The model consists of two layers: a wavelet layer and a multilayer perceptron. The wavelet layer used for adaptive feature extraction in the time–frequency domain is composed of wavelet decomposition and wavelet entropy. The multilayer perceptron used for classification is a feedforward neural network. The performance of the developed system has been evaluated in 215 samples. The test results show that this system is effective to detect Doppler heart sounds. The classification rate averaged 91% correct for 123 test subjects.     

The speaker identification by using genetic wavelet adaptive network based fuzzy inference system

In this paper, an intelligent speaker identification system is presented for speaker identification by using speech/voice signal. This study includes both combination of the adaptive feature extraction and classification by using optimum wavelet entropy parameter values. These optimum wavelet entropy values are obtained from measured Turkish speech/voice signal waveforms using speech experimental set. It is developed a genetic wavelet adaptive network based on fuzzy inference system (GWANFIS) model in this study. This model consists of three layers which are genetic algorithm, wavelet and adaptive network based on fuzzy inference system (ANFIS). The genetic algorithm layer is used for selecting of the feature extraction method and obtaining the optimum wavelet entropy parameter values. In this study, one of the eight different feature extraction methods is selected by using genetic algorithm. Alternative feature extraction methods are wavelet decomposition, wavelet decomposition – short time Fourier transform, wavelet decomposition – Born–Jordan time–frequency representation, wavelet decomposition – Choi–Williams time–frequency representation, wavelet decomposition – Margenau–Hill time–frequency representation, wavelet decomposition – Wigner–Ville time–frequency representation, wavelet decomposition – Page time–frequency representation, wavelet decomposition – Zhao–Atlas–Marks time–frequency representation. The wavelet layer is used for optimum feature extraction in the time–frequency domain and is composed of wavelet decomposition and wavelet entropies. The ANFIS approach is used for evaluating to fitness function of the genetic algorithm and for classification speakers. It has been evaluated the performance of the developed system by using noisy Turkish speech/voice signals. The test results showed that this system is effective in detecting real speech signals. The correct classification rate is about 91% for speaker classification.     

Wavelet networks for sensor signal classification in flank wear assessment

It is known that the force and vibration sensor signals in a turning process are sensitive to the gradually increasing flank wear. Based on this fact, this paper investigates a flank wear assessment technique in turning through force and vibration signals. Mainly to reduce the computational burden associated with the existing sensor-based methods for flank wear assessment, a so-called wavelet network is investigated. The basic idea in this new method is to optimize simultaneously the wavelet parameters (that represent signal features) and the signal-interpretation parameters (that are equivalent to neural network weights) to eliminate the feature extraction phase without increasing the computational complexity of the neural network. A neural network architecture similar to a standard one-hidden-layer feedforward neural network is used to relate sensor signal measurements to flank wear classes. A novel training algorithm for such a network is developed. The performance of this n ew method is compared with a previously developed flank wear assessment method which uses a separate feature extraction step. The proposed wavelet network can also be useful for developing signal interpretation schemes for manufacturing process monitoring, critical component monitoring, and product quality monitoring.     

一种多尺度高光谱影像小波分形维特征计算方法

提出了一种基于高光谱曲线小波分形测度的高光谱影像多尺度分形维特征分析方法。对高光谱影像的光谱响应曲线的小波域高频和低频系数统计特性、分形特征进行了分析,提出以小波低频分形维表征原始光谱曲线分形特征,以小波系数高频分形维表征高光谱细节特征方法,设计了基于高光谱曲线小波分形维的多尺度特征计算算法,实验结果表明,小波分形维值可有效表征丰富的光谱特征,可用于高光谱影像特征提取和分类。     

sEMG信号采集电路设计及其特征提取算法

表面肌电(Surface electromyography,sEMG)信号直接、客观地反映了神经和肌肉的活动功能状态,已获得广泛应用。本文设计了一种sEMG信号采集电路并以单通道形式采集上肢5种动作的sEMG信号,经小波包变换提取6种特征(其中一种引自基于小波变换的特征提取方法)并分别结合PCA和KPCA进行处理;再分别用BP神经网络和SVM进行动作识别。此外,对比了小波变换的特征提取;讨论了KPCA与PCA在特征变换上的差异。所提取的基于小波包变换的6种特征有5种的识别率均超过95.7%,其中引入的高低频系数组合特征在BP神经网络下平均识别率超过99%。基于小波变换提取的5种特征经KPCA变换后也达到较高的识别率。实验结果表明,本文的sEMG信号采集方法及其特征提取方法均达到较好效果。