Mississippi State University
Anderson, Derek T.
Ball, John E.
Younan, Nicholas H.
Date of Degree
Graduate Thesis - Open Access
Electrical and Computer Engineering
Master of Science
James Worth Bagley College of Engineering
Department of Electrical and Computer Engineering
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.
Price, Stanton Robert, "Advanced Feature Learning and Representation in Image Processing for Anomaly Detection" (2015). Theses and Dissertations. 401.