Electronic Theses and Dissertations

Identifier

6661

Author

Monika Shah

Date

2020

Date of Award

12-8-2020

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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