An algorithm for mobile vision-based localization of skewed nutrition labels that maximizes specificity
Vladimir Kulyukin1; Christopher Blay2 1 Department of Computer Science, Utah State University, Logan, UT, USA2 YouTube Corporation, San Bruno, CA, USA
Abstract
An algorithm is presented for mobile vision-based localization of skewed nutrition labels (NLs) on grocery 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, ...
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