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This class library contains more than 20 classes including feature extraction algorithms (MFCC, LPCC) and modeling techniques (HMM, GMM, DTW, VQ ) for automatic speech recognition and speaker verification
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Currently, there are more than 20 classes in this library,
including commonly used feature extraction algorithms and
modeling techniques for speech recognition and speaker verification. -Currently, there are more than 20 classes in this library.
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这个是用Matlab写成的说话人识别演示程序。-Currently, there are more than 20 classes in this library, including commonly used feature extraction algorithms and modeling techniques for speech recognition and speaker verification.-Currently, there are more than 20 classe
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在本文中,主要讲了在语音识别和语音合成之前我们所要做的主要工作，包括去噪,预加重,端点检测,特征参数提取等技术.-In this article, the main speaker in the speech recognition and speech synthesis to be done before we have major work, including de-noising, pre-emphasis, endpoint detection, feature extraction
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DCT用于掌纹识别的word论文文档，比pdf更容易阅读编辑和引用，供大家学习和交流-The discrete cosine transform is a classical image processing technique, and the discriminant analysis is a commonly used image feature extraction technique. These two techniques are effectively combined in
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提出一种新的非线性变换的彩色空间 ″″, 利用次高斯概率分布函数拟合皮肤色度信息, 得到候选区
YC C
r b
域。为了排除候选区域中的非人脸, 首先根据均值和方差信息分割出候选区域中的纹理特征信息, 再通过多尺度
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(
信息定位眼睛, 然后根据人脸特征的几
形态边缘检测算子检测候选区域的边缘, 利用 边缘方向
PCA PCAED
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何形状信息定位其他特征 鼻、嘴 , 通过这些几何特征信息对肤色分割得到的候选区域进行验证, 最终得到正确
的人
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基于灰度共生矩阵的图像检索技术。完整的特征提取、特征描述、特征匹配以及结果返回。-GLCM-based image retrieval techniques. A complete feature extraction, feature descr iption, feature matching and the result returned.
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Abstract—This paper proposes a new technique for face detection
and lip feature extraction. A real-time field-programmable
gate array (FPGA) implementation of the two proposed techniques
is also presented. Face detection is based on a naive Bay
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基于高波滤波的特征提取技术，从而实现人脸识别和表情识别的目的-Based on high-wave filter feature extraction techniques, in order to achieve the purpose of face recognition and expression recognition
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线性判别分析(LDA)是一种较为普遍的用于特征提取的线性分类方法。但是将LDA直接用于人脸识别
会遇到维数问题和“小样本”问题。人们经过研究，通过多种途径解决了这两个问题并实现了基于I,DA的人脸识
别 文章对几种基于LDA的人脸识别方法做了理论上的比较和实验数据的支持，这些方法包括Eigenfaces、Fish—
erfaceS、DLDA、VDLDA及VDFLDA。实验结果表明VDFLDA是其中最好的一种方法。-Low—dimensional feature representat
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研究描述人脸特征的有效方法, 讨论身份证照片的特征提取和检索采用自适应肤色检测技术改进通用的肤色检测算法, 进行脸部区域的划分提出系数投影法对面部五官区域进行分割, 在各区域中提取面部几何特征引人描述脸颊和下额轮廓的曲线参数作为脸形特征, 得到对人脸特征更准确的描述将面部几何特征矢量匹配、脸形曲线参数匹配和脸部图像相关匹配相结合, 实现人像照片的准确检索实验表明该方法性能优良。-Describe the facial features of an effective way to discuss
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该功能主要实现静脉识别中特征提取技术的研究，实现了图像的特征提取匹配过程-The main function of achieving vein recognition feature extraction techniques, but to achieve matching of image feature extraction process
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1．分析研究了基于内容的图像检索系统的工作原理，关键技术如：纹
理、形状等图像底层特征的描述方法， 图像间的相似性度量方法，
图像库索引机制等。
2．研究了基于纹理特征的图像检索方法，并提出了一种基于NSCT 变
换的纹理特征提取方法。通过对SAR 图像及相关图像进行NSCT
分解，计算不同尺度不同方向上的系数幅度序列的均值，标准方差
和三阶中心矩，以此构成特征向量来描述图像的纹理。实验证明本
文提出的采用NSCT 算法有较好的特征提取效果，引入三阶中心矩
作为特
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常用的几种遥感图像特征提取技术分析，新的纹理图像特征提取方法。-Several commonly used feature extraction techniques of remote sensing image
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As the years of intensive research and development went by, and with the birth of
several new conferences and workshops such as IWFHR (International Workshop
on Frontiers in Handwriting Recognition),1 ICDAR (International Conference on
Document
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This chapter gives an introduction to the methods of biomedical image
processing. After some fundamental preliminary remarks to the terminology used,
medical imaging modalities are introduced (Sect. 1.2). Sections 1.3 and 1.4 deal
with low-leve
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ECG Feature Extraction Techniques E CG Feature Extraction Techniques ECG Feature Extraction Techniques ECG Feature Extraction Techniques-ECG Feature Extraction Techniques ECG Feature Extraction Techniques ECG Feature Extraction Techniques ECG Fe
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通过计算机对图像进行去除噪声、增强、复原、分割、提取特征等处理的方法和技术。-Computer image noise removal, enhanced recovery, segmentation, feature extraction processing, such methods and techniques.
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图像检索中常用的特征提取与相似度计算技术，以及基于内容的图像检索系统的实现方法。-That is commonly used in image retrieval feature extraction and similarity calculation techniques, as well as the realization method of content-based image retrieval system.
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An algorithm to recognize distorted Machine and handwritten characters is proposed. It uses a feature point extraction-based recognition approach. A new verification scheme, which deals with this problem, is presented. A new feature extractor set is
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