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Description: 基于文献“一种新的最小割完美匹配算法”的最小割匹配算法-An implementation of a minimum cost perfect matching algorithm described in
Blossom V: A new implementation of a minimum cost perfect matching algorithm.
Vladimir Kolmogorov.
To appear in Mathematical Programming Computation (MPC).
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Author: illusionme |
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Description: 基于文献“对偶凸代价网络流问题算法在计算机视觉中的应用”对于凸网络流问题的一种原始对偶算法的实现-An implementation of the primal-dual method for the dual of the convex network flow problem ("Convex Markov Random Fields") described in
New Algorithms for the Dual of the Convex Cost Network Flow Problem with Application to Computer Vision.
Vladimir Kolmogorov and Akiyoshi Shioura.
Technical Report, June 2007.
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Author: illusionme |
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Description: 基于文献“计算机视觉中的能量函数最小化的最小割/最大流”的最大流算法实现-An implementation of the maxflow algorithm described in
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Computer Vision.
Yuri Boykov and Vladimir Kolmogorov.
In IEEE Transactions on Pattern Analysis and Machine Intelligence, September 2004.
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Author: illusionme |
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Description: kstest_2s_2d.m：功能做两样本的二维Kolmogorov - Smirnov检验
它类似于MATLAB的内置函数的“kstest2“从统计工具箱除了“kstest_2s_2d“与二维数据处理。
[小时，pValue，KSstatistic] = kstest_2s_2d（为x1，x2，α，尾）
函数在两栏输入“ד，并以“ד，其中每一列代表一个维度。其他的选项和产出是相同的含义，是“kstest2“格式。
摘要算法（峰值，1983年）：考虑四个象限（十<的X，Y<Ÿ ），（十<X,y>年），（十>的X，Y<Y)和(x>的X，Y>Ÿ ）反过来，采取了两者之间的经验累积分布的四个最后堪萨斯州最大的统计学差异。
test_kstest_2s_2d.m：试验台的“kstest_2s_2d“
请随时与我联系，如果你发现代码中的任何错误。希望我的理解和实施正确的参考意见...
作者：秋艳鹏@欧洲经委会/香港科技大学
日期：2011年5月11日
参考文献：
茉莉孔雀，英国皇家天文学会每月通告202（1983）615-627“二维拟合优的拟合天文学测试“。-kstest_2s_2d.m: function to do two-sample two-dimensional Kolmogorov-Smirnov test
It s similar to the MATLAB built-in funtion "kstest2" from Statistics Toolbox except that "kstest_2s_2d" deals with two-dimensional data.
[H, pValue, KSstatistic] = kstest_2s_2d(x1, x2, alpha, tail)
The function takes in 2-column inputs "x1" and "x2", where each column represents one dimension. The other options and the outputs are of the same meaning and format as "kstest2".
Algorithm summary (Peak, 1983): consider the four quadrants (x<X,y<Y), (x<X,y>Y), (x>X,y<Y) and (x>X,y>Y) in turn, and adopt the largest of the four differences between the two empirical cumulative distributions as the final KS statistic.
test_kstest_2s_2d.m: test-bench of "kstest_2s_2d"
Please feel free to contact me if you find any bugs in the code. Hope I understood and implemented the ideas in the references correctly...
Author: Qiuyan Peng @ ECE/HKUST
Date: 11th May, 2011
Refere
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Author: abeaqhax |
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Description: 已调试好的数据分布的拟合与检验的matlab程序源代码，内容有卡方拟合优度检验、jbtest函数检验、Kolmogorov-Smirnov检验和画频率直方图-failed to translate
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Author: 胡盛亮 |
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Description: Several matlab Mfiles for the simulation of stochastic processes in finance and engineering, wienner, random walk, brownian motion, two gamble etc. in addition ot solution to forward and backward Chapmann-Kolmogorov equation-Several matlab Mfiles for the simulation of stochastic processes in finance and engineering, wienner, random walk, brownian motion, two gamble etc. in addition ot solution to forward and backward Chapmann-Kolmogorov equation
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Author: sina |
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Description: This software library implements the maxflow algorithm
described in
An Experimental Comparison of Min-Cut/Max-Flow Algorithms
for Energy Minimization in Vision.
Yuri Boykov and Vladimir Kolmogorov.
In IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI),
September 2004
This algorithm was developed by Yuri Boykov and Vladimir Kolmogorov
at Siemens Corporate Research. To make it available for public use,
it was later reimplemented by Vladimir Kolmogorov based on open publications.
If you use this software for research purposes, you should cite
the aforementioned paper in any resulting publication.
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Author: subpic |
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Description: 此代码程序是用来计算kolmogorov的，很便捷使用。-This code program is used to calculate kolmogorov, it is convenient to use.Thank you
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Author: 张长征 |
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Description: 详尽介绍了关联维的计算以及在实际工程中的应用，在短期内有较好的预测性-Detailed descriptions of the calculation of the correlation dimension, as well as in practical engineering applications, and better predictability in the short term
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Author: 王海峰 |
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Description: 这个代码是用 Kolmogorov-Smirnov test计算幂律分布中的度分布概率值的。它既适用于离散数据也适用于连续数据。-This code is a value calculated the probability of the power-law distribution of degree distribution using the Kolmogorov-Smirnov test. Applicable both to the discrete data is also applicable to continuous data.
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Author: vincizhou |
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Description: 实施GRABCUT源代码
由贾斯汀塔尔博特jtalbot@stanford.edu 。
放置在公共领域， 2010年
代码最后更新：2006年
弗拉基米尔·洛夫（ vnk@cs.cornell.edu ） ， 2001年使用GRAPHCUT实施。
要求： OpenGL的， GLUT和OpenCV的库来编译和运行。
用法： grabcut.exe <ppm文件名
使用鼠标拖动矩形围绕前景部分显示的图像。
然后使用下面的按键
1 ：显示图像
2 ：显示GMM组件分配。红色的色调是前景元件 绿色背景组件。
3 ：显示N-链接的权重。白色是一个大型的平均N -链接周围的像素重量，黑色是较低的平均N-链路重量。
4 ：显示T -链接的权重。红色部分是前景T-链路重量，绿色分量为背景的T链接权重。
“” （空格键） ：显示/隐藏计算alpha遮罩。
O ：运行一步GRABCUT细化算法
R ：运行GRABCUT细化算法收敛。
L ：再次运行果园布曼聚类算法。 （在初始化过程中自动运行）。
逃生：停止细化算法（后按 R ）
Q 退出。
-GrabCut implementation source code
by Justin Talbot, jtalbot@stanford.edu
Placed in the Public Domain, 2010
Code last updated, 2006
Uses Graphcut implementation by Vladimir Kolmogorov (vnk@cs.cornell.edu), 2001.
Requires: OpenGL, GLUT, and OpenCV libraries to compile and run.
Usage: grabcut.exe <ppm filename>
Use mouse to drag a rectangle around the foreground portion of the displayed image.
Then use the following keys
1 : Show image
2 : Show GMM component assignment. Red shades are foreground components green are background components.
3 : Show N-link weights. White is a large average N-link weight around a pixel, black is a low average N-link weight.
4 : Show T-link weights. Red component is foreground T-link weight, green component is background T-link weight.
(space bar): Show/hide the computed alpha mask.
o : Run one step of the GrabCut refinement algorithm
r : Run the GrabCut refinement algorithm to convergence.
l : Run the Orchard-
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Description: 判断序列是否为混沌序列的kolmogorov熵值和李雅普诺夫指数计算-Determine whether a sequence is the Kolmogorov entropy of chaotic sequence and calculation of Lyapunov exponent
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Author: 黄子轩 |
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Description: GrabCut实现源代码
贾斯廷·塔尔博特,jtalbot@stanford.edu
放置在公共领域,2010
代码最后更新,2006
使用Graphcut实现弗拉基米尔• 柯尔莫哥洛夫(vnk@cs.cornell.edu),2001。
要求:OpenGL,供过于求,OpenCV库来编译和运行。
用法:grabcut。 exe < ppm文件名>
使用鼠标拖动矩形在前台部分的显示图像。
然后使用以下键
1 :显示图像
“2”:显示组件分配高斯混合模型(GMM)。红色的墨镜是前台组件 绿色是背景组件。
“3”:显示n链接权重。白色是一个大型的平均重量约一个像素n链接,黑色是一个较低的平均n链接权重。
4 :显示t链接权重。红色的组件是前台t链接重量、绿色组件是背景t链接权重。
“(空格键):显示/隐藏计算α面具。
“o”:跑的一个步骤GrabCut细化算法
“r”:运行GrabCut细化算法收敛。
“l”:运行Orchard-Bouman再次聚类算法。 (这是在初始化时自动运行)。
逃脱:停止细化算法(压后“r”)-GrabCut implementation source code
by Justin Talbot, jtalbot@stanford.edu
Placed in the Public Domain, 2010
Code last updated, 2006
Uses Graphcut implementation by Vladimir Kolmogorov (vnk@cs.cornell.edu), 2001.
Requires: OpenGL, GLUT, and OpenCV libraries to compile and run.
Usage: grabcut.exe <ppm filename>
Use mouse to drag a rectangle around the foreground portion of the displayed image.
Then use the following keys
1 : Show image
2 : Show GMM component assignment. Red shades are foreground components green are background components.
3 : Show N-link weights. White is a large average N-link weight around a pixel, black is a low average N-link weight.
4 : Show T-link weights. Red component is foreground T-link weight, green component is background T-link weight.
(space bar): Show/hide the computed alpha mask.
o : Run one step of the GrabCut refinement algorithm
r : Run the GrabCut refinement algorithm to convergence.
l : Run the Orchard-
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Author: 王明 |
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Description: 这个软件图书馆实现了maxflow算法描述
“一个实验比较的最大流最小切/能量最小化算法在视觉”。
尤里Boykov和弗拉基米尔·柯尔莫哥洛夫。
在IEEE模式分析与机器智能(PAMI),
2004年9月
这种算法是由尤里Boykov和弗拉基米尔·柯尔莫哥洛夫
在西门子公司的研究。使它用于公共用途,
后来通过弗拉基米尔·柯尔莫哥洛夫重新实现基于开放的出版物。
如果你使用这个软件为研究目的,你应该引用
上述纸在任何产生的出版物。-This software library implements the maxflow algorithm described in
"An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision."
Yuri Boykov and Vladimir Kolmogorov.
In IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI),
September 2004
This algorithm was developed by Yuri Boykov and Vladimir Kolmogorov
at Siemens Corporate Research. To make it available for public use,
it was later reimplemented by Vladimir Kolmogorov based on open publications.
If you use this software for research purposes, you should cite
the aforementioned paper in any resulting publication.
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Author: 王明 |
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Description: Given a matrix with m rows and n cols (m points in R^n), use resampling and the Kolmogorov Smirnov test to score [0,1] all points (as potential outliers) in linear time.
This is an original algorithm that can be used for anomaly detection and general signal processing.
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Author: markosovs |
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Description: Boykov 和 Kolmogorov 的代码，图像处理 graphcut-Boykov and Kolmogorov code, image processing graphcut
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Author: Betty |
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Description: Maximum Flow Graph (Boykov Kolmogorov Algorithm)
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Author: jimmy |
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Description: 图像超分辨率是指从一序列低分辨率观测图像中重建高分辨率图像。图像超分辨率充分利用了各帧图像
间相似而又不同的信息。-Compared with the model derived from Kolmogorov spectrum, the new model introduces
more complete transcendent constraints and is more similar to the physical characteristics of atmospheric turbulence.
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Author: 王二 |
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Description: This directory contains the MRF energy minimization software accompanying the paper
[1] A Comparative Study of Energy Minimization Methods for Markov Random Fields.
R. Szeliski, R. Zabih, D. Scharstein, O. Veksler, V. Kolmogorov,
A. Agarwala, M. Tappen, and C. Rother.
In Ninth European Conference on Computer Vision (ECCV 2006),
volume 2, pages 16-29, Graz, Austria, May 2006.
Optimization methods included:
1) ICM
2) Graph Cuts
3) Max-Product Belief Propagation
The TRW / TRW-S method is currently not included, but hopefully will be in a future version.
Instructions for compiling and using the software are included below.
CREDITS:
* MRF code, graph cut interface, and example code by Olga Veksler
* Graph cut library by Yuri Boykov and Vladimir Kolmogorov
* Belief propagation code by Marshall Tappen-This directory contains the MRF energy minimization software accompanying the paper
[1] A Comparative Study of Energy Minimization Methods for Markov Random Fields.
R. Szeliski, R. Zabih, D. Scharstein, O. Veksler, V. Kolmogorov,
A. Agarwala, M. Tappen, and C. Rother.
In Ninth European Conference on Computer Vision (ECCV 2006),
volume 2, pages 16-29, Graz, Austria, May 2006.
Optimization methods included:
1) ICM
2) Graph Cuts
3) Max-Product Belief Propagation
The TRW/TRW-S method is currently not included, but hopefully will be in a future version.
Instructions for compiling and using the software are included below.
CREDITS:
* MRF code, graph cut interface, and example code by Olga Veksler
* Graph cut library by Yuri Boykov and Vladimir Kolmogorov
* Belief propagation code by Marshall Tappen
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Author: fant |
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Description: This software library implements the maxflow algorithm described in
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision.
Yuri Boykov and Vladimir Kolmogorov.
In IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI),
September 2004
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Author: 来来 |
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