GrabCut. Interactive Foreground Extraction using Iterated Graph Cuts. Carsten Rother. Vladimir Kolmogorov. Andrew Blake. Microsoft Research Cambridge-UK . GrabCut algorithm was designed by Carsten Rother, Vladimir Kolmogorov their paper, “GrabCut”: interactive foreground extraction using iterated graph cuts. GrabCut: interactive foreground extraction using iterated graph cuts – nadr0/ GrabCut.

Author: Brajinn Nikojinn
Country: Turkmenistan
Language: English (Spanish)
Genre: Sex
Published (Last): 19 May 2013
Pages: 71
PDF File Size: 1.7 Mb
ePub File Size: 3.73 Mb
ISBN: 649-7-37178-834-9
Downloads: 49177
Price: Free* [*Free Regsitration Required]
Uploader: Mazuru

GrabCut -Interactive Foreground Extraction using Iterated Graph Cuts – Microsoft Research

Who likes Messi without his hair? Now our final mask is ready. Then algorithm segments it iteratively to get the best result. Read the Docs v: We need to remove them. And we get a nice result.

See the image below. We will see its arguments first: An algorithm was needed for foreground extraction with minimal user interaction, and the result was GrabCut. Depending on the data we gave, GMM learns and create new pixel distribution. We give the rectangle parameters.

Nodes in the graphs are pixels. Just give some strokes on the images where some faulty results are there.


The weights between the pixels are defined by the edge information or pixel similarity. There we give some 0-pixel touchup sure background.

Then loaded that mask image in OpenCV, edited original mask image we got with corresponding values in newly added mask image. After the cut, all the pixels connected to Source node become foreground and those connected to Sink node become background.

Using brush foregound in the paint, I marked missed foreground hair, shoes, ball etc with white and unwanted background like logo, ground etc with black on this new layer. The cost function is the sum of all weights of the edges that are cut. We will see its arguments first:.

That is, the unknown pixels are labelled either probable foreground or probable background depending on its relation with the other hard-labelled pixels in terms of color statistics It is just like clustering.

Then in the next iteration, you get better results.

Interactive Foreground Extraction using GrabCut Algorithm — OpenCV-Python Tutorials 1 documentation

It labels the foreground and background pixels or extractiob hard-labels Now a Gaussian Mixture Model GMM is used to model the foreground and background. Check the code below: We need to bring it back.


Every foreground pixel is connected to Source node and every background pixel is connected to Sink node. It cuts the graph into interachive separating source node and sink node with minimum cost function.

Also watch this youtube video on how to use it. Initially user draws a rectangle around the foreground region foreground region shoule be completely inside the rectangle.

GrabCut -Interactive Foreground Extraction using Iterated Graph Cuts

First player and football is enclosed in a blue iteratex. In that rgaph, user need to do fine touch-ups. A graph is built from this pixel distribution. Here, you can make this into a interactive sample with drawing rectangle and strokes with mouse, create trackbar to adjust stroke width etc.

Then some final touchups with white strokes denoting foreground and black strokes denoting background is made. It is done by the following flags, cv2.