Chapter 18 - An algorithm for mobile vision-based localization of skewed nutrition labels that maximizes specificity

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, line detection, and corner detection. 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 minimize 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 possible improvements are analyzed and discussed.