Computational visual perception seeks to reproduce human vision
through the combination of visual sensors, artificial intelligence,
and computing. To this end, computer vision tasks are often
reformulated as mathematical inference problems where the objective
is to determine the set of parameters corresponding to the lowest
potential of a task-specific objective function. Graphical models
have been the most popular formulation in the field over the past
two decades where the problem is viewed as a discrete assignment
labeling one. Modularity, scalability, and portability are the main
strengths of these methods which once combined with efficient
inference algorithms they could lead to state of the art results.
This monograph focuses on the inference component of the problem
and in particular discusses in a systematic manner the most
commonly used optimization principles in the context of graphical
models. It looks at inference over low rank models (interactions
between variables are constrained to pairs) as well as higher order
ones (arbitrary set of variables determine hyper-cliques on which
constraints are introduced) and seeks a concise, self-contained
presentation of prior art as well as the presentation of the
current state of the art methods in the field.
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