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对400幅灰度图像用分块PCA的方法进行压缩，进行通信时只用传输主元和特征向量。其中，pcaxiefangcha.m用于图像压缩，imresume.m用于图像恢复和部分图像的显示，chaweight.m用于绘制特征值比重-主元个数关系图，crdraw.m用于绘制压缩比-主元个数关系图，psnrdraw.m用于绘制总误差PSNR-主元个数关系图，psnrlocation.m用于绘制单幅图像误差PSNR分布图。-On the 400 block with a gray image compressi
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主分量分析(PCA ) 是统计学中分析数据的一种有效的方法, 可以将数据从高维数据空间变换到低维特征空间, 因而
可以用于数据的特征提取及压缩等方面。在该文的形状识别系统中, 用PCA 法提取图像的形状特征, 能够较好地满足识别
层的输入要求。在识别层研究了3 种识别方法: 最近邻法则、BP 网络及协同神经网络方法, 均取得了满意的实验效果。-Principal component analysis (PCA) is a statistical analysis of data in a
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matlab
用PCA方法对图像进行压缩、还原-PCA method using matlab image compression, restore
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高光谱图像的一系列处理包括了例如融合压缩pca变换等等等等的程序。-Hyperspectral image processing includes a series of transformations such as the integration of compression pca, etc., etc. procedures.
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用神经网络中的PCA算法对人脸图像进行压缩及恢复-PCA with a neural network algorithm for image compression on the face and back
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This tutorial is designed to give the reader an understanding of Principal Components
Analysis (PCA). PCA is a useful statistical technique that has found application in
fields such as face recognition and image compression, and is a common techn
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这是一个基本的PCA方法实现人脸图像压缩与重建，可以非常快捷的将一些无法辨别的人脸图像进行快速的拼接。-This is a basic PCA method to achieve image compression and reconstruction of the face, can be very quick to identify the human face that can not be quickly spliced images.
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PCA for image compression, Sanger s algorithm implemented with neural networks
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the code conducts the image compression of the gray scale image up to 90 using 4 algos fft wavelet pca and cosine transform-the code conducts the image compression of the gray scale image up to 90 using 4 algos fft wavelet pca and cosine transform
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结合PCA和Wavelet进行图像压缩和特征提取等方面的研究-fuse wavelet and PCA for image compression,denoise and feature extraction
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进行pca提取，实现对于图像的压缩，有利于图像的进一步处理-To pca extract further processing for image compression, image
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2D双向PCA(2D2PCA),二维主成分分析(2DPCA)的实质是对图像矩阵按行进行图像压缩抽取特征,消除了图像列的相关性.-2D bidirectional PCA (2D2PCA), two-dimensional principal component analysis (2DPCA) real image matrix row the Image Compression extraction characteristics to eliminate the correlation of
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用PCA算法在matlab平台上对图像进行压缩与解压缩，内含测试图像，能直接在matlab上运行使用，对学习PCA算法有帮助。-PCA algorithm used in matlab platform for image compression and decompression, containing the test images, can be run directly on matlab, PCA algorithm is helpful for learning.
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自适应PCA 用于信号处理中，在图像压缩，图像识别方面也很有帮助，很有用的代码-, Image compression, image recognition is also very helpful, very useful code for adaptive signal processing in PCA
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内有操作说明： 操作说明.docx
加噪：高斯、椒盐等
去噪：小波、高斯、维纳及频域上的滤波（频率可调）
压缩：JPEG、小波、PCA、位平面、FFT、DCT
以上功能都集成在了GUI界面内-There are instructions: instructions docx plus noise: Gaussian, salt and pepper and other de-noising: wavelet, Gaussian, and Wiener filter in the
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主成分分析算法（PCA），可用于降维，也可用于处理图像相关性问题，提取主成分，分析图像细节信息和主要成分，用于图像压缩也可以-Principal component analysis algorithm (PCA), can be used for dimensionality reduction, can also be used to process images related issues, extracted principal component analysis and main
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Wavelet based Image Reconstruction Gradient Data
Fast morphological reconstruction of large logical masks.
Kernel PCA and Pre-Image Reconstruction
Reconstruction of image projections by Algebraic Reconstruction Technique
Image Reconstruction
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使用matlan对lena图像进行PCA主成分分析压缩。包含变换，还原等。-Use matlan for lena image compression PCA principal component analysis. It contains transformation, reduction and the like.
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PCA实现图像压缩 matlab代码，根据pca选出累计概率达到90%的特征，作为新的特征，并根据新的特征实现图像的重建(PCA implementation of image compression Matlab code)
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Principal components analysis is one of a family of
techniques for taking high-dimensional data, and using the
dependencies between the variables to represent it in a more
tractable, lower-dimensional form, without losing too much
information. PC
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