Description: 最经典之做，保证别人没有上传过
实现文章
“GrabCut" Interactive Foreground Extraction using Iterated Graph Cuts
用graphcut实现图像分割，效果非常好-The most classic to do to ensure that other people do not realize uploaded article GrabCut Interactive Foreground Extraction using Iterated Graph Cuts with graphcut realize image segmentation, has very good results Platform: |
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Description: 与grabcut算法配套的论文，详述了对graphcut算法改进的地方，及其优点，-Matching algorithm with grabcut paper detailing the algorithm graphcut improved, and its advantages, Platform: |
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Description: 一个对GrabCut的实现，代码写得很好，对理解该算法很有帮助-an implementation of GrabCut. the code is very clear and can be a great help for comprehending the algorithm Platform: |
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Description: 三种基于GraphCut的图像分割方法，分别是GrabCut，LazySnapping和改进的GrabCut，其中用到了matlab和VC++的混合编程，在调用之前需生成mex文件-Three kinds of image segmentation method based on GraphCut, respectively GrabCut, LazySnapping and improved GrabCut, which used the matlab and VC++ mixed programming, before calling the mex file to be generated Platform: |
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Description: VC++实现的GrabCut，GrabCut是迭代的GraphCut,分割效果比GraphCut要好，此代码可直接执行的，里面的文件也很全面，值得学习-VC++ implementation GrabCut, GrabCut is iterative GraphCut, segmentation results are better than GraphCut, this code can be executed directly inside the document is very comprehensive and worth learning Platform: |
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Description: 最经典之做，保证别人没有上传过实现文章“GrabCut" Interactive Foreground Exttraction using Iterated Graph Cuts用graphcut实现图图像分割，效果非常好
-The most classic to do, to ensure that others have not uploaded the article " GrabCut" Interactive Foreground Exttraction using Iterated Graph Cuts use graphcut map image segmentation, the effect is very good Platform: |
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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- Platform: |
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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- Platform: |
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Author:王明 |
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