11,493 research outputs found
A Deep-structured Conditional Random Field Model for Object Silhouette Tracking
In this work, we introduce a deep-structured conditional random field
(DS-CRF) model for the purpose of state-based object silhouette tracking. The
proposed DS-CRF model consists of a series of state layers, where each state
layer spatially characterizes the object silhouette at a particular point in
time. The interactions between adjacent state layers are established by
inter-layer connectivity dynamically determined based on inter-frame optical
flow. By incorporate both spatial and temporal context in a dynamic fashion
within such a deep-structured probabilistic graphical model, the proposed
DS-CRF model allows us to develop a framework that can accurately and
efficiently track object silhouettes that can change greatly over time, as well
as under different situations such as occlusion and multiple targets within the
scene. Experiment results using video surveillance datasets containing
different scenarios such as occlusion and multiple targets showed that the
proposed DS-CRF approach provides strong object silhouette tracking performance
when compared to baseline methods such as mean-shift tracking, as well as
state-of-the-art methods such as context tracking and boosted particle
filtering.Comment: 17 page
Bayesian changepoint analysis for atomic force microscopy and soft material indentation
Material indentation studies, in which a probe is brought into controlled
physical contact with an experimental sample, have long been a primary means by
which scientists characterize the mechanical properties of materials. More
recently, the advent of atomic force microscopy, which operates on the same
fundamental principle, has in turn revolutionized the nanoscale analysis of
soft biomaterials such as cells and tissues. This paper addresses the
inferential problems associated with material indentation and atomic force
microscopy, through a framework for the changepoint analysis of pre- and
post-contact data that is applicable to experiments across a variety of
physical scales. A hierarchical Bayesian model is proposed to account for
experimentally observed changepoint smoothness constraints and measurement
error variability, with efficient Monte Carlo methods developed and employed to
realize inference via posterior sampling for parameters such as Young's
modulus, a key quantifier of material stiffness. These results are the first to
provide the materials science community with rigorous inference procedures and
uncertainty quantification, via optimized and fully automated high-throughput
algorithms, implemented as the publicly available software package BayesCP. To
demonstrate the consistent accuracy and wide applicability of this approach,
results are shown for a variety of data sets from both macro- and
micro-materials experiments--including silicone, neurons, and red blood
cells--conducted by the authors and others.Comment: 20 pages, 6 figures; submitted for publicatio
Locating and quantifying gas emission sources using remotely obtained concentration data
We describe a method for detecting, locating and quantifying sources of gas
emissions to the atmosphere using remotely obtained gas concentration data; the
method is applicable to gases of environmental concern. We demonstrate its
performance using methane data collected from aircraft. Atmospheric point
concentration measurements are modelled as the sum of a spatially and
temporally smooth atmospheric background concentration, augmented by
concentrations due to local sources. We model source emission rates with a
Gaussian mixture model and use a Markov random field to represent the
atmospheric background concentration component of the measurements. A Gaussian
plume atmospheric eddy dispersion model represents gas dispersion between
sources and measurement locations. Initial point estimates of background
concentrations and source emission rates are obtained using mixed L2-L1
optimisation over a discretised grid of potential source locations. Subsequent
reversible jump Markov chain Monte Carlo inference provides estimated values
and uncertainties for the number, emission rates and locations of sources
unconstrained by a grid. Source area, atmospheric background concentrations and
other model parameters are also estimated. We investigate the performance of
the approach first using a synthetic problem, then apply the method to real
data collected from an aircraft flying over: a 1600 km^2 area containing two
landfills, then a 225 km^2 area containing a gas flare stack
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