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Learning-Integrated Interactive Segmentation and
Classification of Synthetic Aperature Radar Imagery
Presented by: Stephanie Fonder
ABSTRACT: Segmentation is a low-level task that is a first
step to many computer vision problems. Although image segmentation
algorithms exist which perform reasonably on a limited data
set, a general solution for the image segmentation problem
has not been developed. We present an approach to image segmentation,
in which user selected sets of examples and counter-examples
supply information about the specific segmentation problem
at hand.
In this approach, image segmentation is guided by a genetic
algorithm which learns the appropriate subset and spatial
combination of a collection of discriminating functions, associated
with image features. The genetic algorithm encodes discriminating
functions into a functional template representation, which
can be applied to the input image to produce a candidate segmentation.
The quality of each candidate segmentation is evaluated within
the genetic algorithm, by a comparison to two physics-based
segmentations. Through the process of segmentation, evaluation,
and recombination, the genetic algorithm non-exhaustively
optimizes functional template design. The contributions of
this thesis include: genetic learning of functional template
design, physics-based segmentation evaluation, novel crossover
operator and fitness function, as well as a system prototype
and experiments on synthetic and SAR imagery.
Experimental results demonstrate that evolved templates
select meaningful features, which complement each other for
improved segmentation quality over any single feature. Evolved
segmentations consistently outperform segmentations derived
from the Bayesian best single feature and typically perform
at least as well, if not better than segmentations derived
from the actual best single feature defaults.
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