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4下载:
在Visual C++下实现聚类分析在图像识别中的应用。其中包括模糊聚类,遗传算法聚类分析,层次聚类和动态聚类等算法。可以运行,并有分类的源文件。是不错的研究素材。,In Visual C++ Achieve clustering analysis in image recognition applications. Including fuzzy clustering, genetic algorithm for cluster analysis, hierarchical clusterin
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用k-means 做彩色图像分割,分类数可选,Using k-means to do color image segmentation, classification number of optional
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此方法采用经典的PCA对人脸图像进行特征提取,用libsvm库函数的SVM分类器对图像分类。,This method uses the classical PCA on the face image feature extraction, with the libsvm library function of SVM classifier for image classification.
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MCA算法源程序,可用于稀疏分解,信号分类,图像修复,信号还原-MCA algorithm source code can be used for sparse decomposition, signal classification, image restoration, signal reduction
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对SVM多类分类算法进行了研究,总结了不同分类算法的优缺点。接下来本文提出了基于GA(遗传算法)和KNN(K近邻)的SVM多类分类算法-The SVM multi-class classification algorithms studied, summed up the advantages and disadvantages of different classification algorithms. The next paper, based on GA (genetic algorit
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C均值模糊聚类,适用于图像分类,此为MATLAB 程序代码。-C mean fuzzy clustering for image classification, this is MATLAB code.
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纹理模式的LBP特征表示及分类原理:就是通过定义一个模板,对于原图像中的每个点,使用该模板进行操作,模板中心点近邻区域对应的图像象素值与中心位置对应的象素值进行比较,且对于不同位置的比较结果赋予不同的权值,从而,变换图像中对应点的值就由模板对应各位置的比较结果之权和表示。-LBP texture pattern feature representation and classification theory: that by defining a template for the origina
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线性判别分析(LDA)用于特征选择,可以对数据集或者图像提取有用特征,用于分类或者聚类等机器学习应用中-Linear Discriminant Analysis (LDA) for feature selection, application in dataset or image feature extraction, for classification or clustering applications in machine learning
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图像分类中的交叉验证方法,比如说,一个训练集集合,为了得到其中参数的较准确值,就可以使用此类算法-Image Classification Based on cross-validation method, for example, a collection of training set, in order to obtain more accurate values of these parameters, you can use these algorithms
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对已有图像进行区域识别,画出决策树,并在测试图像上进行测试。-The image of the existing regional recognition, draw the decision tree, and the test images for testing.
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该程序通过遗传算法对图像进行聚类分析,并实现了图像的分类功能,分类效果明显较其他算法好-The genetic algorithm procedure for cluster analysis of the images and to achieve the classification of image features, the classification results significantly better than the other algorithms
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关于rbf神经网络实现图像分类的IEEE英文文献 和大家一起分享-Image Classification using a Module RBF Neural Network
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基于BP神经网络的遥感图像分类代码。从样本中提取崇明岛东滩十种地物的光谱特征,并训练BP网络,再利用网络进行分类-BP neural network-based remote sensing image classification code. Extracted from samples of 10 kinds of Chongming Island, Dongtan features of the spectral characteristics and to train BP networ
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遥感影像分类的matlab实现(源码+图像)。以及分类后评价(总体精度、Kappa系数、混淆矩阵)。-Remote Sensing Image Classification of matlab implementation (source code+ images). After the evaluation and classification (overall accuracy, Kappa coefficient, confusion matrix).
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经典的最大似然法分类法的C语言实现,有助于深入了解遥感分类原理。-This program implements the maximum likelihood classification procedure. ouput:1.classified image, and 2. probability file.
Note: For constructong variance-covariance matrix must be generic binary file.
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基于稀疏编码和线性塔式匹配的图像分类算法。-This package contains the Matlab codes implementing the ScSPM algorithm described in CVPR 09 paper "Linear Spatial Pyramid Matching using Sparse Coding for Image Classification".
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基于svm的图像识别技术,效果不错。可用于图像的分类-Svm-based image recognition technology, good results. Can be used for image classification
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1、编制程序显示印章图像(24位真彩色位图);
2、读出位图中每一像素点的(R,G,B)样本值;
3、以RGB其中某两个(或三个)为坐标,取一定数量的图像点为分析样本,分析其坐标系中的分布;
4、采用本章学习的方法找到分类判别函数,对这些样本进行分类;(要求首先将印章与底纹区分,如有可能将印章、底纹、签字区分)
5、将分类后的结果标记到原始图像上,检查其效果。
-1, the preparation procedures showed that the seal image
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子模式主成分分析首先对原始图像分块,然后对相同位置的子图像分别建立子图像集,在每一个子图像集内使用PCA方法提取特征,建立子空间。对待识别图像,经相同分块后,分别将子图像向对应的子空间投影,提取特征。最后根据最近邻原则进行分类。-Sub-mode principal component analysis first of the original image block, and then the same sub-image, respectively, the location of the
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用于提取图像一些的基本特征,方便进行图像分类-Used to extract some basic characteristics of images to facilitate image classification
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