Visualization and Intelligent Systems Laboratory



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Winston Chung Hall Room 216
University of California, Riverside
900 University Avenue
Riverside, CA 92521-0425

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Bourns College of Engineering
NSF IGERT on Video Bioinformatics

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Keio University

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IEEE Biometrics Workshop 2014
IEEE Biometrics Workshop 2013
Worshop on DVSN 2009
Multibiometrics Book

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Last updated: July 1, 2017



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.