Finally, nine kinds of natural images are classified successfully based on wavelet feature using BP neural network.
文章最后结合九类自然纹理图像，分别基于标准子波特征、子波包特征用BP神经网络进行了分类识别。
Grid feature vector, belonging to the wavelet decomposition sub graph which got by character image wavelet transform, is used to construct character wavelet feature vector.
通过对字符图像的小波分解子图求取网格特征向量，构造出字符的小波特征向量。
This paper mainly make research on classify methods based on statistical theory, support vector machine (SVM), and feature extraction method-wavelet transform, and using them in human face detection.
本文主要研究目前较为流行的基于统计学习理论的分类方法——支持向量机方法(SVM)，以及小波变换提取特征的方法，将其用于人脸检测。
Firstly, Wavelet packet transform is introduced to extract features of vibration measured data and information fusion of data layer is conducted by assembling feature vectors of different sensors.
首先应用小波包变换对结构振动测试数据进行特征提取，通过不同传感器特征向量的合成完成数据层融合；
This paper mainly introduces that the fault vibrating signal of gears was decomposed into time-frequency domains by double-orthogonal wavelet analysis and the fault feature of gears was picked up.
利用双正交小波基将齿轮的故障振动信号分解到时频域，并提取出齿轮的故障特征。
In this method, the original image 's feature is extracted by using wavelet transform and quantization. The feature information is changed into watermark by the chaotic sequence encryption.
该方法在一定的量化标准下利用小渡变换提取图象的特征信息，通过混沌序列对其加密生成数字水印。
At first, all levels energy of wavelet decomposed in the transient are extracted by the means of wavelet analysis, then the extracted feature vectors are classified with RBF neural network.
文中提出了首先用小波分析方法提取出瞬态信号的各级小波分解能量，然后再用r BF神经网络对提取的特征向量进行分类。
The fourth chapter discusses in detail a novel method for edge feature detection of images based on wavelet decomposition and reconstruction by means of interval biorthogonal wavelet.
第四章详细地讨论了利用区间双正交小波，根据小波分解和重构来提取图像的边缘特征的一种新方法。
According to the feature of the MRA, this paper presents a noise filtering method of adaptive fuzzy threshold for wavelet classification base on multi resolution.
针对小波变换多分辨分析(mra)的特点，本文提出一种多尺度分级的自适应模糊权重中值滤波的去噪方法。
This paper combines the two aspects to recognize handwritten digits by using wavelet transform to extract feature and Adaptive Resonance Theory (ART) Neural Networks for Classification.
本文将二者结合起来，用小波变换抽取特征、用自适应共振art网络作模式分类器来识别手写数字。
A fast location algorithm using classifying shrinkage in parameters space and a feature extraction method using improved wavelet cross-zero detection is presented.
提出了参数空间分级收缩的新的定位算法及其改进的小波过零检测的特征提取算法。
Based on the characteristics of wavelet transform, the wavelet Singularity Detection is used to locate the feature point and anomaly extension of the Rayleigh wave dispersion curve.
基于小波变换原理，利用小波分析中的奇异性检测方法来对瑞利波频散曲线的特征点和波动区段定位。
A feature extraction method of high-range-resolution radar profiles, which takes advantage of wavelet packet transform and modified SVD (singular value decomposition) was proposed.
提出了基于小波包变换和改进奇异值分解的高分辨雷达目标一维距离像特征提取方法。
Regarding this, a novel feature level approach to image fusion is proposed based on discrete dyadic wavelet transform for multi-scale image edge detection.
本文提出了一种基于离散二进小波变换的多尺度边缘检测和图像融合的方法，实现了特征级图像融合。
The technique of wavelet transform has been applied and studied in the image coding field extensively because of its good feature of time-frequency and human visual system.
小波变换技术以其良好的空间—频率局部特性和与人眼视觉特性相符的变换机制，在图像编码领域得到了广泛的应用和研究。
This paper discusses the shortage of conventional algorithms of texture classification based on wavelet transform, presents two improved approaches of point feature weighting and smart windows.
在基于小波的纹理分类算法的基础上，提出了逐点特征加权和活动窗口算法，使小波纹理分析能够用于高分辨率遥感影像的分类。
A feature indexing algorithm based on wavelet coefficients is used when comparing features in neighboring images, which increases efficiency in the nearest neighbor searching.
在进行相邻图片的特征比对时，提出一种基于小波系数的特征索引算法，提高搜索效率。
According to the method, the energy of different frequency bands after wavelet packet decomposition constitutes the input vectors of support vector machine as feature vectors.
该方法将振动信号小波包分解后的频带能量作为特征向量，输入到由多个支持向量机构成的多故障分类器中进行故障识别和分类。
A new feature algorithm is proposed based on wavelet transform in HSI space to obtain a feature vector with combining information of color, texture and scale.
提出了基于小波变换的HSI空间的彩色纹理墙地砖图像的特征提取新算法，得到具有颜色、纹理和尺度融合信息的特征矢量。
By means of the wavelet analysis, non-steady signals are analyzed, the fault feature vectors of fault are successfully extracted and this effective method is employed to identify the fault pattern.
研究了小波分析在非平稳信号分析的实际应用，成功地通过小波分析提取故障信号的特征信息，为识别故障类型提供了有效的分析手段。
Concerning the detection of weak signal in stronger noise background, a novel method was proposed in terms of stochastic resonance processing and by using the better de-noise feature of wavelet.
针对强噪声背景中的弱信号检测问题，在经典随机共振处理方法的基础上，利用小波良好的去噪特点，提出了随机共振加小波去噪检测弱信号的新方法。
A joint wavelet transform correlator was proposed for binary image feature extraction.
提出了联合子波变换相关器及其应用于二值图象的特征提取。
A method using two wavelet transform to get the texture feature for remote sensing is put forward, in order to turn out the invariant texture classification for remote sensing in the wavelet domain.
分析了有限脊小波变换可以实现图像的旋转不变性和平移不变性，提出了结合两种小波变换提取图像纹理特征的方法，实现了在小波域中进行图像的不变纹理分类。
The feature vector is composed of wavelet texture energy features, texture features based on the gray-level co-occurrence matrix and the tone of filtered SAR image by using tree wavelet.
该方法将树型小波中颇纹理能量特征、灰度共生矩阵特征、树型小波滤波后的灰度组成的特征矢量对SAR图像进行分类。
Finally, the forecasting results of chaotic models are reconstructed based on wavelet packet theory. By doing so, the forecasting of system feature reference data series can be made.
最后，基于小波包理论将混沌模型预测的结果予以小波包重构，实现对系统特征参数序列的预测。
In the study of brain-computer interfaces, a method based on best basis of wavelet packet decomposition was proposed. The method is used for the feature extraction of electroencephalogram.
在脑机接口研究中，针对脑电特征抽取，提出一种基于小波包最优基分解的方法。
In order to enlarge the difference of fault line and normal line, the complex wavelet pocket band entropy of all sub-frequency bands in feature band is made product.
为了扩大故障线路与正常线路的差别，对特征频带内各子频带的频带熵作积处理，得到了复小波包特征频带复合熵线路特征量化准则。
Wavelet transform has a good analyzing feature and a good time frequency localization. Spectrum whitening is a effective tool of frequency compensation in high resolution processing.
小波变换具有分析性质好和时—频局域化好的特性，而谱白化方法是高分辨处理中一种有效的频率补偿手段。
Wavelet transform has a good analyzing feature and a good time frequency localization. Spectrum whitening is a effective tool of frequency compensation in high resolution processing.
小波变换具有分析性质好和时—频局域化好的特性，而谱白化方法是高分辨处理中一种有效的频率补偿手段。
应用推荐