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Outdoor and Large-Scale Real-World Scene Analysis - 15th International Workshop on Theoretical Foundations of Computer Vision, Dagstuhl Castle, Germany, June 26 - July 1, 2011. Revised Selected Papers (Paperback, 2012 ed.)
Frank Dellaert, Jan-Michael Frahm, Marc Pollefeys, Laura Leal-Taixe, Bodo Rosenhahn
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R1,608
Discovery Miles 16 080
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Ships in 10 - 15 working days
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This book constitutes the thoroughly refereed post-proceedings of
the 15th International Workshop on Theoretic Foundations of
Computer Vision, held as a Dagstuhl Seminar in Dagstuhl Castle,
Germany, in June/July 2011. The 19 revised full papers presented
were carefully reviewed and selected after a blind peer-review
process. The topic of this Workshop was Outdoor and Large-Scale
Real-World Scene Analysis, which covers all aspects, applications
and open problems regarding the performance or design of computer
vision algorithms capable of working in outdoor setups and/or
large-scale environments. Developing these methods is important for
driver assistance, city modeling and reconstruction, virtual
tourism, telepresence, and motion capture.
Factor Graphs for Robot Perception reviews the use of factor graphs
for the modeling and solving of large-scale inference problems in
robotics. Factor graphs are a family of probabilistic graphical
models, other examples of which are Bayesian networks and Markov
random fields, well known from the statistical modeling and machine
learning literature. They provide a powerful abstraction that gives
insight into particular inference problems, making it easier to
think about and design solutions, and write modular software to
perform the actual inference. This book illustrates their use in
the simultaneous localization and mapping problem and other
important problems associated with deploying robots in the real
world. Factor graphs are introduced as an economical representation
within which to formulate the different inference problems, setting
the stage for the subsequent sections on practical methods to solve
them. The book explains the nonlinear optimization techniques for
solving arbitrary nonlinear factor graphs, which requires
repeatedly solving large sparse linear systems. Factor Graphs for
Robot Perception will be of interest to students, researchers and
practicing roboticists with an interest in the broad impact factor
graphs have had, and continue to have, in robot perception.
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