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Description: 基于图像对的三维重建，利用sift和区域增长算法的稠密匹配以达到三维重建的目的-Image-based three-dimensional reconstruction of the use of sift and regional growth of the dense matching algorithm in order to achieve the purpose of three-dimensional reconstruction
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Description: extract dense sift for each image patch, because no salient keypoint detection and rotation normalization, it is very efficient.
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Size: 2048 |
Author: 郭恺 |
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Description: mit（csail）上的matlab程序，里面的c++程序需要mex编译，算法可参考下面这篇论文-SIFT flow: dense correspondence across difference scenes ECCV2008
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Author: liucun |
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Description: 代码用于寻找图像间的匹配点。先使用SIFT匹配算法得到两幅图像间的稀疏匹配点，然后再使用quasi-dense propagation算法得到quasi-dense匹配点。代码使用C++编写。
-This program is used to find point matches between two images. The procedure can be divided into two parts:
1) Firstly, using SIFT matching algorithm to find sparse point matches between two images.
2) Secondly, using "quasi-dense propagation" algorithm to get "quasi-dense" point matches. "Quasi-dense" means the matches are distributed evenly, and have quantities been improved significantly.
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Author: liutailei |
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Description: We show that i s possible to estimate depth from two wide baseline images using a dense descriptor. Our local descriptor, called DAISY, is very fast and efficient to compute. It depends on histograms of gradients like SIFT and GLOH but uses a Gaussian weighting and circula-We show that it is possible to estimate depth from two wide baseline images using a dense descriptor. Our local descriptor, called DAISY, is very fast and efficient to compute. It depends on histograms of gradients like SIFT and GLOH but uses a Gaussian weighting and circula
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Author: na |
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Description: 图像的特征用到了Dense Sift，通过Bag of Words词袋模型进行描述，当然一般来说是用训练集的来构建词典，因为我们还没有测试集呢。虽然测试集是你拿来测试的，但是实际应用中谁知道测试的图片是啥，所以构建BoW词典我这里也只用训练集。
其实BoW的思想很简单，虽然很多人也问过我，但是只要理解了如何构建词典以及如何将图像映射到词典维上去就行了，面试中也经常问到我这个问题，不知道你们都怎么用生动形象的语言来描述这个问题？
用BoW描述完图像之后，指的是将训练集以及测试集的图像都用BoW模型描述了，就可以用SVM训练分类模型进行分类了。
在这里除了用SVM的RBF核，还自己定义了一种核： histogram intersection kernel，直方图正交核。因为很多论文说这个核好，并且实验结果很显然。能从理论上证明一下么？通过自定义核也可以了解怎么使用自定义核来用SVM进行分类。-Image features used in a Dense Sift, by the Bag of Words bag model to describe the word, of course, the training set is generally used to build the dictionary, because we do not test set. Although the test set is used as the test you, but who knows the practical application of the test image is valid, so I am here to build BoW dictionary only the training set.
In fact, BoW idea is very simple, although many people have asked me, but as long as you understand how to build a dictionary and how to image map to the dictionary D up on the line, and interviews are often asked me this question, do not know you all how to use vivid language to describe this problem?
After complete description of the image with BoW, refers to the training set and test set of images are described with the BoW model, the training of SVM classification model can be classified.
Apart from having to use the RBF kernel SVM, but also their own definition of a nuclear: histogram intersection kernel, histogram
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Author: lipiji |
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Description: 整个场景和应用的密集通讯。 IEEE模式分析与机器智能（TPAMI），2010。
如果您使用您的研究，我们的代码，请举出我们的报纸。另外，请注意到有包ECCV版本相比略有变化。已获得密集的SIFT特征mexed。
请运行demo.h第一。如果出现错误，请去“mexDenseSIFT”和的“mexDiscreteFlow”子文件夹，并按照readme.txt文件的说明（是的，有readme.txt文件中的每个文件夹）编译cpp文件。-Dense Correspondence across Scenes and its Applications. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2010.
Please cite our paper if you use our code for your research. Also, please notice that there is slight change in the package compared to the ECCV version. Obtaining dense SIFT features has been mexed.
Please run demo.h first. If error appears, please go to "mexDenseSIFT" and "mexDiscreteFlow" subfolders and follow the instructions in readme.txt (yes, there is readme.txt in each folder) to compile the cpp files.
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Size: 685056 |
Author: cf |
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Description: vlfeat,包含SIFT图像特征提取算法，matlab接口，可以进行dense sift和普通sift，核心部分由c实现，计算高效。-Vlfeat, contains the SIFT image feature extraction algorithm, Matlab interface, can be dense sift and general sift, the core part is implementated by C, computational efficiency.
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Size: 10235904 |
Author: lixiafeng |
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Description: 基于图像对的三维重建，利用sift和区域增长算法的稠密匹配以达到三维重建的目的-Sift and regional growth algorithm based on three-dimensional reconstruction of the image on dense matching in order to achieve the purpose of the three-dimensional reconstruction
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Size: 29696 |
Author: 余添 |
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Description: sift特征提取，提取dense-sift特征，快速，易懂，很不错的-sift feature extraction, extraction dense-SIFT features, fast, easy to understand, very good
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Size: 2048 |
Author: 郭芙蓉 |
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Description: 大牛 E Tola的daisy特征描述子 比sift稠密 是稠密匹配经典之作 速度很快-Daniel E Tola s daisy feature descriptor dense is dense than sift matching classic fast
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Author: 飞翔 |
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Description: 用于图像特征点提取的稠密型sift,很好的一种特征点提取-Dense type sift for image feature extraction, a good feature point extraction
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Size: 3072 |
Author: 彭沛沛 |
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Description: SIFT flow: dense correspondence across different scenes.
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Author: LI HUA |
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Description: 基于VC++的双目立体视觉检测 采用SIFT特征点计算稀疏匹配 和稠密匹配-VC++ based binocular stereo vision detection using SIFT feature points to calculate sparse matching and dense matching
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Author: xuyong |
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Description: multifocus image fusion using dense sift
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Size: 1864704 |
Author: asmaagad |
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Description: C. Liu, J. Yuen and A. Torralba. SIFT Flow: Dense Correspondence across Scenes and its Applications. 对应的SIFT Flow代码，转载而来。(this is the code of SIFT Flow, and this code is reproduced from Ce Liu. Run demo.m in MATLAB and you will see how SIFT flow works.)
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Author: xx126
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