Date of Award:

5-2013

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Computer Science

Committee Chair(s)

Vladimir A. Kulyukin

Committee

Vladimir A. Kulyukin

Committee

Curtis Dyreson

Committee

Nick Flann

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 independence. 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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