An algorithm for mobile vision-based localization of skewed nutrition labels that maximizes specificity - Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition - page 231

FIGURE 10 Case 2: Intersection D is above two normals.
FIGURE 11 Case 3: Intersection D is below two normals.
4 Experiments
4.1 Complete and Partial True Positives
We assembled 378 images captured from a Google Nexus 7 Android 4.3 smartphone during
a typical shopping session at a local supermarket. Of these images, 266 contained an NL and
112 did not. Our skewed NL localization algorithm was implemented and tested on the same
platform with these images.
We manually categorized the results into five categories: complete true positives, partial
true positives, true negatives, false positives, and false negatives. A complete true positive is
an image where a complete NL was localized. A partial true positive is an image where only
a part of the NL was localized by the algorithm. Figure 12 shows examples of complete and
partial true positives.
FIGURE 12 Complete (left) vs. partial (right) true positives.
Figure 13 shows another example of complete and partial true positives. The image on the
left was classified as a complete true positive, because the part of the NL that was not detec-