Dissertation/Thesis Abstract

Advanced feature learning and representation in image processing for anomaly detection
by Price, Stanton Robert, M.S., Mississippi State University, 2015, 110; 1586997
Abstract (Summary)

Techniques for improving the information quality present in imagery for feature extraction are proposed in this thesis. Specifically, two methods are presented: soft feature extraction and improved Evolution-COnstructed (iECO) features. Soft features comprise the extraction of image-space knowledge by performing a per-pixel weighting based on an importance map. Through soft features, one is able to extract features relevant to identifying a given object versus its background. Next, the iECO features framework is presented. The iECO features framework uses evolutionary computation algorithms to learn an optimal series of image transforms, specific to a given feature descriptor, to best extract discriminative information. That is, a composition of image transforms are learned from training data to present a given feature descriptor with the best opportunity to extract its information for the application at hand. The proposed techniques are applied to an automatic explosive hazard detection application and significant results are achieved.

Indexing (document details)
Advisor: Anderson, Derek T.
Commitee: Ball, John E., Younan, Nicolas H.
School: Mississippi State University
Department: Electrical and Computer Engineering
School Location: United States -- Mississippi
Source: MAI 54/04M(E), Masters Abstracts International
Subjects: Electrical engineering
Keywords: Feature learning, Image processing, Object detection, Pattern recognition, Soft features, improved Evolution-COnstructed
Publication Number: 1586997
ISBN: 978-1-321-69807-7
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