A Comparative Study of Energy Minimization Methods for Markov Random Fields

  title={A Comparative Study of Energy Minimization Methods for Markov Random Fields},
  author={Richard Szeliski and Ramin Zabih and Daniel Scharstein and Olga Veksler and Vladimir Kolmogorov and Aseem Agarwala and Marshall F. Tappen and Carsten Rother},
One of the most exciting advances in early vision has been the development of efficient energy minimization algorithms. Many early vision tasks require labeling each pixel with some quantity such as depth or texture. While many such problems can be elegantly expressed in the language of Markov Random Fields (MRF’s), the resulting energy minimization problems were widely viewed as intractable. Recently, algorithms such as graph cuts and loopy belief propagation (LBP) have proven to be very… CONTINUE READING
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