Description: 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 Platform: |
Size: 1406163 |
Author:站长 |
Hits:
Description: 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. including commonly used feature extraction al gorithms and modeling techniques for re speech cognition and speaker verification. Platform: |
Size: 1127171 |
Author:原琴 |
Hits:
Description: 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 Platform: |
Size: 1405952 |
Author:站长 |
Hits:
Description: 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. including commonly used feature extraction al gorithms and modeling techniques for re speech cognition and speaker verification. Platform: |
Size: 1126400 |
Author:原琴 |
Hits:
Description: 特征提取技术。这种技术以传统的建模方式为前提，对于产生的模型的几何、拓扑等信息加以分析以提取其特征信息，这种技术一般多采用人工智能及模式识别等方面的知识。-Feature extraction techniques. This technology to the traditional modeling approach for the premise, the model generated geometry, topology and other information to be analyzed to extract its characteristic information, the technology in general use areas such as artificial intelligence and pattern recognition knowledge. Platform: |
Size: 55296 |
Author:dingkkkk |
Hits:
Description: 基于bp神经网络的文字识别系统本，主要包括图像预处理和网络识别2部分，图像预处理部分包含许多图像处理技术，然后是特征的提取，其结果利用bp神经网络进行训练识别，是对图像处理和模式识别进行综合学习的好资料！-Bp neural network based text recognition system of this, including image pre-processing and network identification part 2, image pre-processing part consists of many image processing techniques, followed by feature extraction, resulting in the use of bp neural network trained to identify, is the image processing and pattern recognition integrated learning good information! Platform: |
Size: 59392 |
Author:bryan |
Hits:
Description: 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 techniques. Platform: |
Size: 3084288 |
Author:chengbin |
Hits:
Description: 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 the paper, and a new palmprint feature extraction approach is proposed. After performing discrete cosine transform on the palmprint images, the proposed approach first uses a two-dimensional separability judgment to select the frequency bands with favorable separability. Then from the selected bands, it extracts the discriminant features by using an improved Fisherface method. The experimental results on the public palmprint image database demonstrate the effectiveness of the proposed approach. Platform: |
Size: 49152 |
Author:小哈 |
Hits:
Description: 语音识别技术主要包括特征提取技术、模式匹配准则及模型训练技术三个方面。此外，还涉及到语音识别单元的选取。选择识别单元是语音识别研究的第一步。语音识别单元有单词（句）、音节和音素三种，具体选择哪一种，由具体的研究任务决定。-Speech recognition technology, including feature extraction techniques, pattern-matching criteria and the three aspects of model training techniques. In addition, it involves the selection of speech recognition modules. Choose identification unit is the first step in speech recognition research. Speech recognition module has the word (sentence), three types of syllables and phonemes, the specific selection of which, by the specific research mission decide. Platform: |
Size: 8192 |
Author:candy |
Hits:
Description: 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
)
(
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
( )
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
的人脸区域。利用3 个实验数据集测试该算法, 并与其它相应的算法相比较, 提出的非线性彩色空间对于肤色分
割具有很好的效果, 且对光照和姿态具有良好的不变性。另外, 利用 信息和几何特征信息检测人脸特征
PCAED
具有很高的定位精度, 定位检测率优于其他方法。实验结果表明, 该算法具有定位准确率高, 漏检率和误检率低
等特点。-
A novel approach for skin segmentation and facial feature extraction is proposed
The proposed skin segmentation is a method for integrating the chrominance components of
″″ . ″″
r b r b
nonlinear YC C color model The chrominance components of nonlinear YC C color space
,
are modeled using a subgaussian probability density function and then the face skin is seg
. ,
mented based on this function In order to authenticate the face candidate regions firstly tex
ture information in face candidate regions is segmented using mean and variance of luminance
, . ,
information and then the eye is located by the PCA edge direction information Finally the
, ,
others features such as nose and mouth also are detected using the geometrical shape infor
. 2 ,
mation As all the above mentioned techniques are simple and efficient the skin segmentation
.
based on nonlinear color spacemethod has the invariability of lighting and pose In the experi
, .
ments themethod has been successfull Platform: |
Size: 458752 |
Author:zz |
Hits:
Description: This paper identifies a novel feature space to
address the problem of human face recognition from
still images. This based on the PCA space of the
features extracted by a new multiresolution analysis
tool called Fast Discrete Curvelet Transform. Curvelet
Transform has better directional and edge
representation abilities than widely used wavelet
transform. Inspired by these attractive attributes of
curvelets, we introduce the idea of decomposing
images into its curvelet subbands and applying PCA
(Principal Component Analysis) on the selected
subbands in order to create a representative feature
set. Experiments have been designed for both single
and multiple training images per subject. A
comparative study with wavelet-based and traditional
PCA techniques is also presented. High accuracy rate
achieved by the proposed method for two well-known
databases indicates the potential of this curvelet based
feature extraction method.-This paper identifies a novel feature space to
address the problem of human face recognition from
still images. This is based on the PCA space of the
features extracted by a new multiresolution analysis
tool called Fast Discrete Curvelet Transform. Curvelet
Transform has better directional and edge
representation abilities than widely used wavelet
transform. Inspired by these attractive attributes of
curvelets, we introduce the idea of decomposing
images into its curvelet subbands and applying PCA
(Principal Component Analysis) on the selected
subbands in order to create a representative feature
set. Experiments have been designed for both single
and multiple training images per subject. A
comparative study with wavelet-based and traditional
PCA techniques is also presented. High accuracy rate
achieved by the proposed method for two well-known
databases indicates the potential of this curvelet based
feature extraction method. Platform: |
Size: 432128 |
Author:Swati |
Hits:
Description: 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 classes in this library. including commonly used feature extraction al gorithms and modeling techniques for re speech cognition and speaker verification. Platform: |
Size: 2451456 |
Author:liximin |
Hits:
Description: 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 Platform: |
Size: 19272704 |
Author:hefangfang |
Hits:
Description: This thesis relates to the design, implementation and evaluation of statis¬ tical face recognition techniques. In particular, the use of Hidden Markov Models in various forms is investigated as a recognition tool and critically evaluated. Current face recognition techniques are very dependent on issues like background noise, lighting and position of key features (ie. the eyes, lips etc.). Using an approach which specifically uses an embedded Hidden Markov Model along with spectral domain feature extraction techniques, shows that these dependencies may be lessened while high recognition rates are maintained. Platform: |
Size: 1726464 |
Author:ivan |
Hits:
Description: 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 Platform: |
Size: 1271808 |
Author:付江文 |
Hits:
Description: 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 introduced. This research explores best sets of feature extraction techniques and studies the accuracy and speed of the suggested procedures. Finally, experimental results on one are presented, their results showing the robustness of the algorithm using small .-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 introduced. This research explores best sets of feature extraction techniques and studies the accuracy and speed of the suggested procedures. Finally, experimental results on one are presented, their results showing the robustness of the algorithm using small . Platform: |
Size: 109568 |
Author:mohammed |
Hits:
Search - feature extraction techniques