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这次上传的代码是关于特征提取的主要算法之一:ica,其比pca要好-this code is uploaded on the main feature extraction algorithm : ica, better than the PCA
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人脸识别是生物特征识别技术中一个非常活跃的课题,取得了很多研究成果。统计主元分析法( Prin2cipal ComponentsAnalysis, PCA)是人脸特征提取和识别的常用方法之一。-Face recognition is an active subject in the area of biometrical recognition technology, and lots of achievements have
been obtained. Principal Compone
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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 Transfo
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提出一种基于主分量分析和相融性度量的快速聚类方法。通过构造主分量空间将高维数据投影到两个主成分上
进行特征提取,每一个主分量都是原始变量的线性组合-Is proposed based on Principal Component Analysis and Measure of blending fast clustering method. Principal component space by constructing a high-dimensional data onto two p
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人脸识别技术是计算机模式识别领域非常活跃的研究课题,在法律、商业等领域有
着广泛的应用前景。自动人脸识别系统一般由两个模块组成:定位与检测模块,特征提
取与识别模块。本文对两个子模块进行了详细讨论,通过实验仿真了一个基于静态图像
的人脸识别系统。为提高系统的识别率,本文对定位检测模块和特征提取模块进行了深
入研究。
针对复杂多变人脸检测和定位问题,实现了一种基于对称特征的人脸定位方法。该
算法首先基于肽色特征提取出人脸区域,根据眼睛的颜色和梯度特征在肤色区找到眼睛
可
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PCA feature extraction in handwritten digit recognition.
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提出了一种复杂背景下的多车牌图像分割和识别方法,首先采用统计和特征匹配相结合的方法进行背景提取,将可能存在车辆的区域提取出来;然后分别对可能的车辆区域进行局部边缘检测,并使用车牌的先验知识确定车牌的位置和单个字符分割,包括车牌倾斜时的字符分割;最后使用PCA和神经网络相结合的方法精确识别车牌。-Proposed a multi-plate image segmentation and recognition method under a complex background, the first
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基于PCA特征提取和距离哈希K近邻分布的人脸表情识别-PCA-based feature extraction and distribution of K-nearest neighbor distance hash Facial Expression Recognition
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When extracting discriminative features multimodal
data, current methods rarely concern the data distribution.
In this paper, we present an assumption that is consistent with
the viewpoint of discrimination, that is, a person’s overall
biomet
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人脸识别技术作为生物体特征识别技术的重要组成部分,在近些年来已经发展成为计算机视觉和模式识别领域的研究热点。本实验是基于K-L变换的主成分分析法(PCA)在人脸识别中的应用,在ORL人脸库的基础上通过Matlab实现了快速PCA算法的验证仿真,并对样本图像进行了重构。本实验在ORL人脸库的基础上,选用每人前5张图片,共计40人200幅样本图像,通过快速PCA算法将10304维的样本特征向量降至20维,并实现了基于主分量的人脸重建,验证了PCA算法在高维数据降维处理与特征提取方面的有效性。-Fac
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是学习PCA特征提取的很好的学习资料,采用热核构造权重,插值与拟合,解方程,数据分析。- Is a good learning materials to learn PCA feature extraction, Thermonuclear using weighting factors Interpolation and fitting, solution of equations, data analysis.
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有信道编码,调制,信道估计等,是学习PCA特征提取的很好的学习资料,包括最后计算压缩图像的峰值信噪比和压缩效果的源码。- Channel coding, modulation, channel estimation, Is a good learning materials to learn PCA feature extraction, Including the final calculation of the compressed image peak signal to noise ra
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分形维数计算的毯子算法matlab代码,是学习PCA特征提取的很好的学习资料,可以提取一幅图中想要的目标。- Fractal dimension calculation algorithm matlab code blankets, Is a good learning materials to learn PCA feature extraction, Target can be extracted in a picture you want.
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语音信号的采集与处理,数字信号处理课设,是学习PCA特征提取的很好的学习资料,是国外的成品模型。- Acquisition and Processing of the speech signal, digital signal processing class-based, Is a good learning materials to learn PCA feature extraction, Foreign model is finished.
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人脸识别中的光照处理方法,是学习PCA特征提取的很好的学习资料,可以提取一幅图中想要的目标。- Face Recognition light treatment method, Is a good learning materials to learn PCA feature extraction, Target can be extracted in a picture you want.
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进行逐步线性回归,用平面波展开法计算二维声子晶体带隙,是学习PCA特征提取的很好的学习资料。- Stepwise linear regression, Computation Method D phononic bandgap plane wave, Is a good learning materials to learn PCA feature extraction.
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正确率可以达到98%,是学习PCA特征提取的很好的学习资料,使用起来非常方便。- Accuracy can reach 98 , Is a good learning materials to learn PCA feature extraction, Very convenient to use.
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均值便宜跟踪的示例,是学习PCA特征提取的很好的学习资料,实现六自由度运动学逆解算法。- Example tracking mean cheap, Is a good learning materials to learn PCA feature extraction, Six degrees of freedom to achieve inverse kinematics algorithm.
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最大信噪比的独立分量分析算法,是学习PCA特征提取的很好的学习资料,isodata 迭代自组织的数据分析。- SNR largest independent component analysis algorithm, Is a good learning materials to learn PCA feature extraction, Isodata iterative self-organizing data analysis.
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鲁棒性好,性能优越,光纤无线通信系统中传输性能的研究,是学习PCA特征提取的很好的学习资料。- Robustness, superior performance, Fiber Transmission wireless communication system performance, Is a good learning materials to learn PCA feature extraction.
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