Efficient Parallel Estimation for Markov Random Fields
  We present a new, deterministic, distributed MAP estimation algorithm for
Markov Random Fields called Local Highest Confidence First (Local HCF). The
algorithm has been applied to segmentation problems in computer vision and its
performance compared with stochastic algorithms. The experiments show that
Local HCF finds better estimates than stochastic algorithms with much less
computation.
