An Algorithm for Mobile Vision-Based Localization of Skewed Nutrition Labels that Maximizes Specificity Vladimir Kulyukin Department of Computer Science Utah State University Logan, UT, USA Christopher Blay YouTube, Inc Palo Alto, CA, USA vkedco.blogspot.com
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An Algorithm for Mobile Vision-Based Localization of Skewed Nutrition Labels that Maximizes Specificity
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An Algorithm for Mobile Vision-Based Localization of Skewed Nutrition Labels that Maximizes Specificity
Introduction● Many nutritionists consider proactive nutrition
management to be a key factor in reducing and controlling cancer and diabetes
● According to the U.S. Department of Agriculture, U.S. residents have increased their caloric intake by 523 calories per day since 1970
● Enabling consumers to use computer vision on smartphones to extract nutritional information from nutrition labels (NLs) will likely result in improved nutritional decisions
Relaxation of Alignment Constraints● In our previous work (Kulyukin et al., IPCV 2013), we
developed a vision-based algorithm for horizontally or vertically aligned NLs on smartphones (pdf)
● This algorithm improves our previous algorithm in that it handles not only aligned NLs but also the NLs that are skewed up to 35-40 degrees from the vertical axis of the captured frame
● This algorithm is designed to improve specificity, i.e., percentage of true negative matches out of all possible negative matches
● Modern nutrition management system designers and developers assume that users understand how to collect nutritional data and can be triggered into data collection with digital prompts
● Many users find it difficult to integrate nutritional data collection into their daily activities due to lack of time, motivation, or training
● The current algorithm is a step in the direction of automating nutritional data collection and analysis