Dissertation/Thesis Abstract

On mobile detection and localization of skewed nutrition facts tables
by Blay, Christopher, M.C.S., Utah State University, 2013, 54; 1550140
Abstract (Summary)

With about 3.6 million adults in the United States having visual impairment or blindness, assistive technology is essential to give these people grocery shopping independance. This thesis presents a new method to detect and localize nutrition facts tables (NFTs) on mobile devices more quickly and from less-ideal inputs than before. The method is a drop-in replacement for an existing NFT analysis pipeline and utilizes multiple image analysis methods which exploit various properties of standard NFTs.

In testing, this method performs very well with no false-positives and 42% total recall. These results are ideal for real-world application where inputs are analyzed as quickly as possible. Additionally, this new method exposes many possibilities for future improvement.

Indexing (document details)
Advisor: Kulyukin, Vladimir A.
Commitee: Dyreson, Curtis, Flann, Nick
School: Utah State University
Department: Computer Science
School Location: United States -- Utah
Source: MAI 52/04M(E), Masters Abstracts International
Subjects: Computer science
Keywords: Assistive technology, Computer vision, Image recognition, Nutrition facts tables
Publication Number: 1550140
ISBN: 978-1-303-64148-0
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