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scale invariant features transform matlab code
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本代码是用来检测图像的局部不变特征的，主要参考DAVID G. LOWE的sift的论文。变成工具是matlab。-This code is used to detect the image of local invariant features, the main reference DAVID G. Lowe s sift paper. Become a tool is Matlab.
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PCA-SIFT的matlab源码，可以对图像的SIFT特征进行PCA主成成分提取，达到降维的目的-PCA-SIFT matlab source image SIFT features the PCA main into ingredients extracted achieve dimensionality reduction purposes
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SIFT特征点检测算法，matlab实现的，程序里还有匹配部分功能-SIFT feature point detection algorithm, matlab implementation, program as well as some features match
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关于sift检测特征的程序，程序比较多比较集中-About sift detection features, programs are more concentrated
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根据局部尺度不变特征编写的MATLAB仿真程序，经验证可以正确运行。-Prepared according to the local invariant features scale MATLAB simulation program, and is certified to run correctly.
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SIFT特征具有缩放、旋转特征不变性，下载了大牛的matlab版SIFT特征提取代码，解释如下：appendimages-Matlab version on SIFT SIFT features with zoom, rotation invariant features, download large cattle extraction code, interpreted as follows: appendimages
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matlab 程序，由sift算法提出者提供的程序，可以在作者本人的个人主页上下载到-Distinctive Image Features Scale-Invariant Keypoints
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提取图像sift特征，实现图像局部特征识别，用matlab语言写的一个简易demo-Extracting the features of sift image, realizing the image local feature recognition, using MATLAB language to write a simple demo
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VLFeat是一个跨平台的开源机器视觉库，它囊括了当前流行的机器视觉算法，如SIFT, MSER, HOG, 同时还包含了诸如K-MEANS, Hierarchical K-means的聚类算法。它由C语言编写，并提供了Matlab接口及详细的文档。当前最新的版本是VLFeat 0.9.18 。(The VLFeat open source library implements popular computer vision algorithms specializing in image un
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sift算子，提取区域特征，进行对象的检测与跟踪。(Sift operator is used to extract region features and detect and track objects.)
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%this code is the Matlab implimentation of David G. Lowe,
%"Distinctive image features from scale-invariant keypoints,"
%International Journal of Computer Vision, 60, 2 (2004), pp. 91-110.
%this code should be used only for academic res
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用matlab语言实现sift算法寻找图像特征点的程序(detect features of images with SIFT)
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MATLAB实现图像的SIFT特征提取，并做在不同光照、不同视角下的特征匹配(MATLAB realizes SIFT feature extraction of image and matches features under different illumination and different visual angles)
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