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daviddoria / GrabCut

NOTE: Image segmentation that iteratively uses Expectation Maximization for Gaussian Mixture Model estimation and Graph Cuts.

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GrabCut

Image segmentation using GMM EM and Graph Cuts An implementation of the work described here: http://cvg.ethz.ch/teaching/cvl/2012/grabcut-siggraph04.pdf

Getting Started

You can run GrabCutExample data/soldier.png data/soldier_selection.fbmask result.png to run an example segmentation for yourself.

Build notes

This code depends on c++0x/11 additions to the c++ language. For Linux, this means it must be built with the flag gnu++0x (or gnu++11 for gcc >= 4.7).

Dependencies

  • ITK >= 4
  • Boost >= 1.51 You can tell this project's CMake to use a local boost build with: cmake . -DBOOST_ROOT=/home/doriad/build/boost_1_51
  • Eigen >= 3.2 You can tell this project's CMake to use a local Eigen build with: cmake . -DEIGEN3_INCLUDE_DIR=/home/doriad/src/eigen-3.2.1/

About

NOTE: Image segmentation that iteratively uses Expectation Maximization for Gaussian Mixture Model estimation and Graph Cuts.

Languages

Language:C++ 91.6%Language:CMake 8.4%