Description: 运动估计代码.里面有源图像和运行结果.对写论文应该有帮助-motion estimation code. Active inside images and results of operations. Papers should be written to help Platform: |
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Description: 使用光流法进行的视频图像分割The Lucas-Kanade algorithm for motion estimation was used, and was implemented in Matlab due to the fact that most of the matrix operation functions that are needed are already available and hence saves time by not having to implement these functions again. -The use of optical flow method of video image segmentation The Lucas-Kanade algorithm for motion estimation was used, and was implemented in Matlab due to the fact that most of the matrix operation functions that are needed are already available and hence saves time by not having to implement these functions again. Platform: |
Size: 256000 |
Author:selina |
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Description: 这是一个基于opencv的计算光流的程序，采用的是Lucas-Kanade算法-This is a calculation based on optical flow opencv procedures, using the Lucas-Kanade algorithm Platform: |
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Author:liwei |
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Description: Lucas-Kanader-Tomasi Feature Tracker，由运动恢复结构的问题，目的是从一组由摄像机移动获得的图像来恢复一个场景的立体结构。对于这个问题，C.Tomasi和T.Kanade[1]于1992年提出了一种Factorization的方法，可以有效地避免噪声的干扰，同时不会受限于特定的运动模型，例如单纯的平移或是旋转。-Lucas-Kanader-Tomasi Feature Tracker, by the movement to restore the structure of the problem, aimed at a group of mobile access by the camera image to restore the three-dimensional structure of a scene. For this problem, C. Tomasi and T. Kanade [1] in 1992 proposed a factorization method which can effectively avoid noise interference, while not restricted to a particular motion model, such as pure translation or rotation . Platform: |
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Author:梁霄 |
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Description: Lucas-Kanader-Tomasi Feature Tracker，基于B.D.Lucas和T.Kanade[1]于1981的早期工作，于1991年由C.Tomasi和T.Kanade[2]完整导出，并于1994年由J.Shi和C.Tomasi[3]在文章中清楚地阐述。J.Y.Bougue[4]于2000年给出了此算法的一种实现，并收录在Intel公司的计算机视觉函数库OpenCV中。本例是此算法基于OpenCv的代码实现-Lucas-Kanader-Tomasi Feature Tracker, based on BDLucas and T. Kanade [1] in 1981 the early work, in 1991 by C. Tomasi and T. Kanade [2] a complete export, and was established in 1994 by J. Shi and C . Tomasi [3] in the article clearly explained. JYBougue [4] in 2000, given a realization of this algorithm and is included in Intel s computer vision library OpenCV in. This case is that this algorithm is based on the code OpenCv Platform: |
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Author:梁霄 |
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Description: 这可是由Takeo Kanade 撰写的人脸识别方面的文章。研究人脸表情识别与人脸识别的都需要这篇文章，这也是难得的国外资源啊！-This is written by Takeo Kanade Face Recognition articles. Research on Facial Expression Recognition and Face Recognition need this article, this is a rare foreign resources ah! Platform: |
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Author:刘秋菊 |
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Description: Deaf people use facial expressions a non-manual channel for conveying grammatical information in sign language. Tracking facial features using the Kanade - Lucas - Tomasi (KLT) algorithm is a simple and effective method toward recognizing these facial expressions, which are performed simultaneously with head motions and hand signs. To make the tracker robust under these conditions, a Bayesian framework was developed as a feedback mechanism to the KLT tracker. This mechanism relies on a set of face shape subspaces learned by Probabilistic Principal Component Analysis. An update scheme was utilized to modify these subspaces and adapt to persons with different face shapes. The result shows that the proposed system can track facial features with large head motions, substantial facial deformations, and temporary face occlusions by hand.-Deaf people use facial expressions as a non-manual channel for conveying grammatical information in sign language. Tracking facial features using the Kanade- Lucas- Tomasi (KLT) algorithm is a simple and effective method toward recognizing these facial expressions, which are performed simultaneously with head motions and hand signs. To make the tracker robust under these conditions, a Bayesian framework was developed as a feedback mechanism to the KLT tracker. This mechanism relies on a set of face shape subspaces learned by Probabilistic Principal Component Analysis. An update scheme was utilized to modify these subspaces and adapt to persons with different face shapes. The result shows that the proposed system can track facial features with large head motions, substantial facial deformations, and temporary face occlusions by hand. Platform: |
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Author:Ng Jack |
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Description: Lucas-Kanade part 2 对计算机视觉和机器学习很有帮助 详细论文和代码在文章的附录中有详细地址 很不错 -Lucas-Kanade part 2 of the computer vision and machine learning for more helpful articles and code in the article addresses in detail in the appendix is very good Platform: |
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Author:匣子 |
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Description: Real time optical flow, and video processing, with support for camera input in Matlab.-Start by running "runMe.m"
Supports input through:
a) synthetically generated test images
b) reading avi-file
c) camera input (requires image aquisition toolbox)
Calculates optical flow by 4 different local methods(all moment based), 2 implementations of Lucas and Kanade, and 2 implementations of point-line flow.
Can process easily hundreds of frames per second on a laptop for resolution 200x200 images. Platform: |
Size: 3751936 |
Author:史明明 |
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Description: Traditional optical flow technique that developed by Lucas and Kanade was improved with the optimized variable setting. It is quite simple and also helpful. Platform: |
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Author:JUHWAN LEE |
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Description: 这是KLT算法论文的高清版。Kanade-Lucas-Tomasi方法，在跟踪方面表现的也不错，尤其在实时计算速度上，用它来得到的，是很多点的轨迹“trajectory”，并且还有一些发生了漂移的点，所以，得到跟踪点之后要进行一些后期的处理，说到Kanade-Lucas-Tomasi方法，首先要追溯到Kanade-Lucas两人在上世纪80年代发表的paper：An Iterative Image Registration Technique with an Application to Stereo Vision，这里讲的是一种图像点定位的方法，即图像的局部匹配，将图像匹配问题，从传统的滑动窗口搜索方法变为一个求解偏移量d的过程，后来Jianbo Shi和Carlo Tomasi两人发表了一篇CVPR(94 )的文章Good Features To Track，这篇文章，主要就是讲，在求解d的过程中，哪些情况下可以保证一定能够得到d的解，这些情况的点有什么特点（后来会发现，很多时候都是寻找的角点）。-KLT is an implementation, in the C programming language, of a feature tracker for the computer vision community. The source code is in the public domain, available for both commercial and non-commerical use.
The tracker is based on the early work of Lucas and Kanade [1], was developed fully by Tomasi and Kanade [2], and was explained clearly in the paper by Shi and Tomasi [3]. Later, Tomasi proposed a slight modification which makes the computation symmetric with respect to the two images-- the resulting equation is derived in the unpublished note by myself [4]. Briefly, good features are located by examining the minimum eigenvalue of each 2 by 2 gradient matrix, and features are tracked using a Newton-Raphson method of minimizing the difference between the two windows. Multiresolution tracking allows for relatively large displacements between images. The affine computation that evaluates the consistency of features between non-consecutive frames [3] was implemented by Thorsten T Platform: |
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Author:王凯 |
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