GRABCUT INTERACTIVE FOREGROUND EXTRACTION USING ITERATED GRAPH CUTS PDF

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.

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Using brush tool 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. So we will give there a fine touchup with 1-pixel sure foreground. Then loaded that mask image in OpenCV, edited original mask image we got with corresponding values in newly added mask image.

extracction Everything inside rectangle is unknown. Who likes Messi without his hair? There we give some 0-pixel touchup sure background. We will see its arguments first: Also watch this youtube video on how to use it. It is illustrated in below image Image Courtesy: Just give some strokes on the images where some faulty results are there.

Read the Docs v: We give the rectangle parameters. We create fgdModel and bgdModel. The process is continued until the classification converges.

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

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. We will see its arguments first:. User inputs the rectangle.

Then directly apply the grabCut function with mask mode. We need to bring it back. The weights between the pixels are defined by intsractive edge information or pixel similarity. We need to remove them. An algorithm was needed for foreground extraction with minimal user interaction, and the result was GrabCut. You just create two np. Then in the next iteration, you get better results. Mode should be cv2. The cost function is the sum of all weights of the edges that are cut.

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

Everything outside this rectangle will be taken as sure background That is the reason it is mentioned before that your rectangle should include all the objects.

Now we go for grabcut algorithm with OpenCV. Let the algorithm run for 5 iterations. Then filled remaining background with gray. After the cut, all the pixels connected to Source node become foreground and those connected to Sink node become background.

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It labels the foreground and background pixels or it hard-labels Now a Gaussian Mixture Model GMM is used to grzph the foreground and fooreground. Then algorithm segments it iteratively to get the best result.

Initially user draws a rectangle around the foreground region foreground region shoule be completely inside the itfrated. Computer does an initial labelling depeding on the data we gave.

GrabCut -Interactive Foreground Extraction using Iterated Graph Cuts

Additional two nodes are added, Source node and Sink node. We load the image, create a similar mask image. Then run the grabcut.

Here, you can make this into a interactive sample with drawing rectangle and strokes with mouse, create trackbar to adjust stroke width etc. If there is a large difference in pixel color, the edge between them will get a low iteratdd. In that case, user need to do fine touch-ups. See the image below.

OpenCV has the function, cv2. How it works from user point of view?