This book introduces a Clausius Normalized Field (CNF) based on a
novel homology between thermodynamic systems and images for the
treatment of time-varying imagery, and we also present CNF modeling
methods for motion segmentation and uncalibrated stereo matching
problems. A CNF is a probabilistic model which reckons entropy
variations by observing entropy definitions of Clausius and
Boltzmann. A system colder than its surroundings absorbs heat from
the surroundings, and the absorbed heat increases the entropy of
the system, according to the entropy definition of Clausius. The
increased entropy is also highly related to the disorder of the
system as given by the entropy definition of Boltzmann. Because the
pixels of an image are viewed as a state of lattice-like molecules
in a thermodynamic system, reckoning the entropy variations of
pixels is similar to estimating the degrees of disorder of the
pixels.
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