Description: Distinctive Image Features from Scale-Invariant Keypoints David G. Lowe
这是一个很好的图象匹配算法（SIFT），同时能处理亮度、平移、旋转、尺度的变化，利用特征点来提取特征描述符，最后在特征描述符之间寻找匹配。
-Distinctive Image Features from Scale-Invariant Keypoints
David G. Lowe
这是一个很好的图象匹配算法（SIFT），同时能处理亮度、平移、旋转、尺度的变化，利用特征点来提取特征描述符，最后在特征描述符之间寻找匹配。
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Author:Deng Fu Qin |
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Description: 两份SIFT的重要资料：Distinctive Image Features from Scale-Invariant Keypoints（by David G. Lowe）,SIFT特征匹配技术讲义(by 赵辉)-SIFT two important information: Distinctive Image Features from Scale-Invariant Keypoints (by David G. Lowe), SIFT feature matching technical notes (by Zhao Hui) Platform: |
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Description: 该文献提出了一种从图像中提取不变特征的方法，即我们通常所说的SIFT方法。-This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, Platform: |
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Author:许飞扬 |
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Description: 本文主要介绍了如何提取图像特征，并进行图像特征匹配-This paper presents a method for extracting distinctive invariant features from
images that can be used to perform reliable matching between different views of
an object or scene. Platform: |
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Author:路元元 |
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Description: 英文资料 Distinctive Image Features from Scale-Invariant Keypoints-aaaDistinctive Image Features from Scale-Invariant Keypoints_2004 by David lowe Platform: |
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Description: SIFT(Scale Invariant Feature Transform)即尺度不变特征变换，是 D. G.Lowe 在 1999 年提出的一种基于图像局部特征的描述算子，并于 2004年做了完善。SIFT算法是一种基于线性尺度空间，对图像缩放、旋转甚至仿射变换保持不变的局部特征描述算子，因此被广泛地应用于机器人定位、导航和地图生成中。-This paper presents a method for extracting distinctive invariant features from images that can be used
to perform reliable matching between different views of an object or scene. The features are invariant to image scale
and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in
3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm,followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to r Platform: |
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Description: %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 research.
%any other useage of this code should not be allowed without Author agreement.
% if you have any problem or improvement idea about this code, please
% contact with Xing Di, Stevens Institution of Technology. xdi2@stevens.edu Platform: |
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Author:arzehgar
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