Electronic Theses and Dissertations

Identifier

6661

Monika Shah

Date

2020

Document Type

Thesis

Degree Name

Master of Science

Major

Electrical and Computer Engr

Concentration

Computer Engineering

Committee Member

Bonny Banerjee

Committee Member

Aaron L Robinson

Committee Member

Madhusudhanan Balasubramanian

Abstract

Assuming the brain is a prediction machine, we investigate the arrangement of 2D visual features (Gabor filters) such that visual prediction can be carried out efficiently. The contributions of this thesis are threefold: (1) we prove that a 1-mode tensor can be predicted from an n-mode and an (n-1)-mode tensors; (2) using a linear model to predict visual features at varying distances from a given location, we show that prediction error increases smoothly and monotonically with increase in distance and increase in degree of sparsity of the prediction model; (3) we show that the visual features can be arranged in a 2D grid such that the total wiring length can be minimized without compromising prediction accuracy. All experiments are carried out using 110 high-resolution natural images.

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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