Fast and Exact Algorithms for Energy Minimization
We present different graph cut-based approaches for exact optimization of some Markovian energies. We reformulate these energies as binary Markov random fields asscotiated with each level sets of an image. First we consider the case where data fidelity terms are convex functions and where the prior is the total variation. Then we generalize this approach to the case of "levelable" energies. A second generalization, different from the first one, consider the case where priors are convex functions. Finally, we present an efficient minimization algorithm for energies where both data fidelities and priors are convex functions. Numerical experiments are presented for each case.
