Date of Award:

2013

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Computer Science

Advisor/Chair:

Vladimir A. Kulyukin

Abstract

With about 3.6 million adults in the United States having visual impairment or blind- ness, 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.

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