Electronic Theses and Dissertations

Identifier

6238

Date

2018

Date of Award

11-27-2018

Document Type

Thesis

Degree Name

Master of Science

Major

Electrical and Computer Engr

Committee Member

Aaron Robinson

Committee Member

Eddie Jacobs

Committee Member

Madhusudhanan Balasubramanian

Abstract

Compressive Sensing (CS) has proven its ability to reduce the number of measurements required to reproduce images with similar quality to those reconstructed by observing the Shannon-Nyquest sampling criteria. By exploiting spatial redundancies, it was shown that CS can be used to denoise and enhance image quality. In this thesis we propose a method that incorporates an efficient use of CS to locate a specific object in zero-visibility environments. This method was developed after multiple implementations of dictionary learning, reconstruction, detection, and tracking algorithms in order to identify the shortcomings of existing techniques and enhance our results. We show that with the use of an over-complete dictionary of the target our technique can perceive the location of the target from hidden information in the scene. This thesis will summarize the previously implemented algorithms, detail the shortcomings evident in their outputs, explain our setups, and present quantified results to support its efficacy in the results section.

Comments

Data is provided by the student.

Library Comment

dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.

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