Image Processing Reference
In-Depth Information
penalty of this method could be quite high, it could allow for the detection and dewarping of
oddly shaped NLs. Finally, a more careful analysis of the found Hough lines during the early
rotation correction could allow us to detect and localize NLs of all possible rotations, not just
skewed ones.
The U.S. Food and Drug Administration recently proposed some changes to the design of
NLs on product packages [ 15 ] . The new design is expected to change how serving sizes are
calculated and displayed. Percent daily values are expected to shift to the left side of the NL,
which allegedly will make them easier to read. The new design will also require information
about added sugars as well as the counts for Vitamin D and potassium. We would like to em-
phasize that this redesign, which is expected to take at least 2 years, will not impact the pro-
posed algorithm, because the main tabular components of the new NL design will remain the
same. The nutritional information in the new NLs will still be presented textually in rows and
columns. Therefore, the corner and line detection and their projections will work as they work
on the current NL design [ 16 ] .
[1] Anding R. Nutrition made clear. Chantilly, VA: The Great Courses; 2009.
[2] Rubin AL. Diabetes for dummies. 3rd ed. Hoboken, NJ: Wiley, Publishing, Inc; 2008.
[3] Nutrition Labeling and Education Action of 1990. htp://
tion_Labeling_and_Education_Act_of_1990 .
[4] Food Labelling to Advance Beter Education for Life. Available at .
[5] Kulyukin V, Kutiyanawala A, Zaman T, Clyde S. Vision-based localization and text
chunking of nutrition fact tables on android smartphones. In: Proceedings of the Interna-
tional Conference on Image Processing, Computer Vision, and Patern Recognition
(IPCV 2013); Las Vegas, NV: CSREA Press; 2013:1-60132-252-6314-320.
[6] Nicholson J, Kulyukin V. ShopTalk: independent blind shopping = verbal route direc-
tions + barcode scans. In: Proceedings of the 30th Annual Conference of the Rehabilita-
tion Engineering and Assistive Technology Society of North America (RESNA 2007),
June 2007, Phoenix, Arizona; 2007 Avail. on CD-ROM.
[7] Kulyukin V, Kutiyanawala A. Accessible shopping systems for blind and visually
impaired individuals: design requirements and the state of the art. Open Rehabil J.
2010;2:158-168. doi:10.2174/1874943701003010158 ISSN 1874-9437.
[8] Kulyukin V, Kutiyanawala A, Zaman T. Eyes-free barcode detection on smartphones with
Niblack's binarization and support vector machines. In: Proceedings of the 16th Interna-
tional Conference on Image Processing, Computer Vision, and Patern Recognition
(IPCV 2012), Vol. 1; Las Vegas, NV: CSREA Press; 2012:284-290 July 16-19. ISBN
1-60132-223-2, 1-60132-224-0.
[9] Kulyukin V, Zaman T. Vision-based localization of skewed UPC barcodes on smartphones.
In: Proceedings of the International Conference on Image Processing, Computer Vi-
sion, & Patern Recognition (IPCV 2013); Las Vegas, NV: CSREA Press;
2013:1-60132-252-6344-350 314-320.
[10] Fog BJ. A behavior model for persuasive design. In: Proceedings of the 4th International
Conference on Persuasive Technology, Article 40; New York, USA: ACM; 2009.
[11] Canny JF. A computational approach to edge detection. IEEE Trans Patern Anal Mach
Intell. 1986;8:679-698.
[12] Duda RO, Hart PE. Use of the hough transformation to detect lines and curves in pic-
tures. Comm. ACM. 1972;15:11-15 January.
Search WWH ::

Custom Search