Automatic target recognition organized via jump-diffusionalgorithms
Miller, M.I.; Grenander, U.; OSullivan, J.A.; Snyder, D.L.
Image Processing, IEEE Transactions on
Volume 6, Issue 1, Jan 1997 Page(s):157 - 174
Digital Object Identifier 10.1109/83.552104
Summary:Proposes a framework for simultaneous detection, tracking, and
recognition of objects via data fused from multiple sensors. Complex
dynamic scenes are represented via the concatenation of simple rigid
templates. The variability of the infinity of pose is accommodated via
the actions of matrix Lie groups extending the templates to individual
instances. The variability of target number and target identity is
accommodated via the representation of scenes as unions of templates of
varying types, with the associated group transformations of varying
dimension. We focus on recognition in the air-to-ground and
ground-to-air scenarios. The remote sensing data is organized around
both the coarse scale associated with detection as provided by tracking
and range radars, along with the fine scale associated with pose and
identity supported by high-resolution optical, forward looking infrared
and delay-Doppler radar imagers. A Bayesian approach is adopted in which
prior distributions on target scenarios are constructed via dynamical
models of the targets of interest. These are combined with physics-based
sensor models which define conditional likelihoods for the coarse/fine
scale sensor data given the underlying scene. Inference via the Bayes
posterior is organized around a random sampling algorithm based on
jump-diffusion processes. New objects are detected and object identities
are recognized through discrete jump moves through parameter space, the
algorithm exploring scenes of varying complexity as it proceeds. Between
jumps, the scale and rotation group transformations are generated via
continuous diffusions in order to smoothly deform templates into
individual instances of objects
View citation and abstract |
Cart
