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。
The extraction of effective feature vector and pattern recognition is the key technique in the process of mechanical condition monitoring and diagnosis.
有效特征向量的提取和状态识别是设备状态监测与故障诊断中的关键技术。
According to features of edge gradient maps of face images, a new eye feature extraction algorithm based on gradient vector flow field was proposed.
根据人脸图像的边缘梯度图提出了一种新的基于梯度向量流场的眼睛特征提取方法。
An automatic seal identification scheme is proposed, which USES the adaptive feature extraction and support vector machine.
文章提出了一种基于自适应特征提取和支撑向量机的印鉴自动识别方法。
By using calibrated image, it can measure plane absolute displacement of worktable automatically via feature point extraction and gray vector matching.
该方法采用事先通过标定的图像，对自然标志(特征点)的提取和基于灰度向量匹配来完成绝对位移的测量。
Applied BP neural network to recognize Numbers, and adopted a method of wipe off miscellaneous points to take out noise, and used a method of per pix feature extraction to extract feature vector.
应用BP神经网络对数字进行识别，其图像的预处理采用去除杂点方法去除噪声，使用逐像素特征提取方法进行特征向量的提取。
A novel method of overlap feature extraction technology is be proposed to pick-up feature vector which has rich information.
为了提取包含丰富特征信息的特征矢量，本文提出了一种新的特征重叠抽取技术。
It constructs document feature vector of subject and Keyword separately by using a new method of document feature extraction.
使用新的文档特征抽取方法构造了文档的主题和关键字特征向量。
Based on RGB color space, this text gives the method of feature extraction and the method of compute the feature vector.
本文给出了基于RGB颜色空间的特征提取方法和特征向量的色差计算方法。
Accordingly, we propose an improved feature extraction scheme, adopting the tone of filtered image combined with the texture features based on the GLCM of unfiltered image to form the feature vector.
针对这种情况，提出了一种改进的特征提取方法，将基于原图像的灰度级共生矩阵提取的纹理特征与滤波后图像的灰度特征进行组合用于分类。
Finally, a method based on sub-band feature extraction and Support Vector Machine with Binary Tree Architecture (SVM-BTA) is presented for power quality disturbances multi-classification.
最后，提出了一种基于子频带特征提取和二叉树结构支持向量机相结合的电能质量多分类方法。
It is significant to study feature vector. This thesis focuses on feature extraction and feature selection.
有效的特征向量是实现字符准确识别的前提，因此具有重要的研究意义。
Wavelet packet Analysis is applied to feature extraction of ultrasonic signals, and the support vector machine is employed to perform the identification task.
然后利用支持向量机分类方法对缺陷进行识别。
Experimental results show that the feature extraction approach remarkably reduces the dimensionality of the input vector while the characteristics of the signals have been reserved.
实验结果表明，该信号测量和抽取算法在有效保留信号精度的同时，显蓍地减少了信号特征向量的维数。
After analyzing their properties in time and frequency domain, we propose two feature-extraction methods and performed some classification experiments based on support vector machine. Ther...
在分析两类飞机时域、频域特性的基础上给出了两种特征提取方法，利用支持矢量机进行了识别实验，结果表明所提出的方法是可行的。
After analyzing their properties in time and frequency domain, we propose two feature-extraction methods and performed some classification experiments based on support vector machine. Ther...
在分析两类飞机时域、频域特性的基础上给出了两种特征提取方法，利用支持矢量机进行了识别实验，结果表明所提出的方法是可行的。
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