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。
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.
针对核电站中检测跌落零件误报警率高的问题，本文对带通滤波后的跌落零件冲击信号应用了基于统计特征的小波去噪方法，并给出了去噪后的跌落零件报警方法。
In order to improve the alarm accuracy rate in nuclear power station, this paper adopts Wavelet De noising (WD) method based on statistic characteristics and presents a loose parts alarm method.
本文分析了线性随机系统解过程的小波特征，得到若干性质，并分析了小波展开系统的统计特征及相关程度。
In this paper, the author study wavelet properties of solution processes of linear random system, and analyse their statistics properties and relation rate, the author obtain some new results.
该系统先对粮虫图像进行小波边缘提取，根据灰度共生矩阵和局部统计方法提取小波分割后的图像纹理特征。
Edge detction based on wavelet multi-scale identity is made. The statistics features based on regional gray and the co-occurrence matrix of gray level are taken as performing image segmentation.
在对图像纹理特征进行统计分析的基础上，本文提出了一种基于纹理分析的图像小波变换清晰度评价方法。
An image definition criterion using wavelet transform based on the texture analysis was proposed in this paper through the statistical analysis to the image texture characteristic.
在纹理特征提取方面，针对不同纹理特点分别采用了基于共生矩阵的统计纹理分析和基于小波变换的频谱纹理分析两种方法予以实现。
Similarly, the work of texture feature extraction is obtained by using co-occurrence matrix or frequency analysis based on wavelet transform depending on different characteristics of images.
在纹理特征提取方面，针对不同纹理特点分别采用了基于共生矩阵的统计纹理分析和基于小波变换的频谱纹理分析两种方法予以实现。
Similarly, the work of texture feature extraction is obtained by using co-occurrence matrix or frequency analysis based on wavelet transform depending on different characteristics of images.
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