Interactive image segmentation sara vicente 1 s vicente adastral ucl ac uk
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T B. T U. T F. Interactive image segmentation Sara Vicente 1 ([email protected]) Supervised by Vladimir Kolmogorov 1 and Carsten Rother 2 1 University College London, 2 Microsoft Research Cambridge.

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Interactive image segmentation sara vicente 1 s vicente adastral ucl ac uk

TB

TU

TF

Interactive image segmentation

Sara Vicente 1([email protected])

Supervised by Vladimir Kolmogorov 1 and Carsten Rother 2

1 University College London, 2 Microsoft Research Cambridge

The aim of interactive image segmentation is to extract an object from an image by segmenting the image in two regions: background and foreground.

To minimize the problems of fully automatic segmentation, a user imposes some hard constraints: a lasso or rectangle around the object or the specification of regions that have to be part of background or foreground.

GrabCut overview

Model Constraints

Input

Goal

Iterative algorithm

Computes segmentation using a standard minimum cut algorithm

Updates in each iteration the colour model for background and foreground based on last iteration

Colour agreement: colour of the pixel should agree with the colour model of the label assigned to it (colour models are computed for background and foreground)

Regional coherence: neighbour pixels should be assigned the same label, especially if the colour of both is similar.

Different weights can be given to the two components of the model producing very distinct results.

Assign to each pixel a label

0 – background, 1 – foreground

dividing the image in two regions

First iteration

User Input Trimap:

TF – foreground

TU – unknown region

TB – background

Extreme settings: exaggerated colour agreement weight

Extreme settings: exaggerated regional coherence weight

Last iteration

Improving GrabCut: introducing flux

GrabCut “shrinking” effect

Results with flux

For some images, GrabCut algorithm has a shrinking effect, cutting elongated structures.

It was proven in [1] that it is possible to integrate the optimization of flux in the GrabCut framework. This integration should prevent this shrinking effect to happen.

The choice of the vector field for which we intend to optimize the flux should be done carefully in order to achieved the desirable results.

Future work

References:

[1] Vladimir Kolmogorov and Yuri Boykov. What metrics can be approximated by geocuts, or global optimization of length/area and flux. In ICCV ’05, 2005.

[2] C. Rother, V. Komogorov, and A. Blake, “GrabCut” - Interactive foreground extraction using iterated graph cuts. In ACM Transactions on Graphics (SIGGRAPH'04), 2004

  • Development and test of new vector fields that can be used for the flux optimization

  • Learn parameters of the model: weights of the different components (agreement with data, regional coherence and flux)

  • Evaluation of the new model using a more complete database of images


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