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Description: 本混合高斯模型是基于opencv的,利用了系统自带的检测函数,检测效果较好.欢迎切磋!-The mixed Gaussian model is based on the opencv, using built-in detection system function, better detection. Welcome to learn!
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Author: yi wen |
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Description: 行人检测源程序，居于svm技术。和梯度直方图提取-Pedestrian Detection source, living in SVM technology. And gradient histogram extraction
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Author: 李可 |
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Description: 混合高斯背景建模用于检出背景，并提取前景，在OPENCV环境下操作进行的。是数字图像处理领域不可缺少的一种处理方式。OPENCV库里含有的函数在数字图像中的作用都是不可获取的，用起来简单又便捷，建议OPENCV从头认真学起-Gaussian mixture background modeling for the detection of background and foreground extraction, OPENCV environment in the operation. The field of digital image processing an indispensable approach. OPENCV library containing the function s role in the digital images are not obtained, it is simple and easy to use, it is recommended to learn the OPENCV seriously from the beginning.
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Author: yuanyuan |
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Description: OpenCV中，利用C语言写的GMM模型的代码。-OpenCV, the use of the GMM model code written in C language.
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Author: luer |
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Description: 介绍Opencv这个图像处理库环境下的GMM建模与EM算法，从数学的角度深入分析高斯建模和EM算法-Introduce the the Opencv this image processing GMM modeling the EM algorithm in the library environment, in-depth analysis from a mathematical point of the Gaussian model and the EM algorithm
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Author: xue |
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Description: 在这一节中就采用opencv自带的一个EM sample来学习下opencv中EM 算法类的使用，顺便也体验下EM 算法的实际应用。
环境：Ubuntu12.04+Qt4.8.2+QtCreator2.5+opencv2.4.2
在这里需要使用2个与EM算法有关的类，即CvEM和CvEMParams，这2个类在opencv2.4.2已经放入legacy文件夹中了，说明不久就会被淘汰掉，因为在未来的opencv版本中，将采用Algorithm这个公共类来统一接口。不过CvEM和CvEMParams的使用与其类似，且可以熟悉EM算法的使用流程。
需要注意的是这2个类虽然是与EM算法有关，可是只能解决GMM问题，比较局限。也许这是将其放在legacy中的原因吧。
可以参考本篇博客来学习：http://www.cnblogs.com/tornadomeet/archive/2012/07/16/2592953.html-In this section, the opencv comes with an EM sample to learn the use of the EM algorithm under opencv class, by the way, experience under the practical application of the EM algorithm. Environment: Ubuntu12.04+Qt4.8.2+QtCreator2.5+opencv2.4.2 here need to use two class EM algorithm, namely CvEM and CvEMParams two classes in opencv2.4.2 into the legacy file folder shows soon will be eliminated, because in future versions of opencv Algorithm this public class unified interface. However, use of CvEM and CvEMParams its similar, and familiar with the use of the EM algorithm process. Should be noted that the two classes and to the EM algorithm, but can only solve the GMM problem, more limited. Perhaps this is put the legacy of the reasons. You can reference this blog to learn: http://www.cnblogs.com/tornadomeet/archive/2012/07/16/2592953.html
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Author: wuwei |
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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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Author: liu |
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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: 经典背景减除方法，MOG2(或GMM)，从opencv2.4.9中单独提取出来的，经过实验调试通过的，可同其他背景减除方法结合或对比。-It is a typical background subtraction mathod, which is OpenCV 2.4.9. It was proposed by Zivkovic in 2014ICPR and proved effective.
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Author: wang bingshu |
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