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An algorithm for mobile
vision-based localization of
skewed nutrition labels that
maximizes specificity
Vladimir Kulyukin 1 ; Christopher Blay 2
1 Department of Computer Science, Utah State University, Logan, UT, USA
2 YouTube Corporation, San Bruno, CA, USA
An algorithm is presented for mobile vision-based localization of skewed nutrition labels (NLs) on gro-
cery packages that maximizes specificity, i.e., the percentage of true negative matches out of all possible
negative matches. The algorithm works on frames captured from the smartphone camera's video stream
and localizes NLs skewed up to 35-40° in either direction from the vertical axis of the captured frame.
The algorithm uses three image processing methods: edge detection, line detection, and corner detec-
tion. The algorithm targets medium- to high-end mobile devices with single or quad-core ARM systems.
Since cameras on these devices capture several frames per second, the algorithm is designed to minim-
ize false positives rather than maximize true ones, because, at such frequent frame capture rates, it is far
more important for the overall performance to minimize the processing time per frame. The algorithm
is implemented on the Google Nexus 7 Android 4.3 smartphone. Evaluation was done on 378 frames, of
which 266 contained NLs and 112 did not. The algorithm's performance, current limitations, and pos-
sible improvements are analyzed and discussed.
Computer vision
Nutrition label localization
Mobile computing
Text spoting
Nutrition management
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