1. Ogden C, Carrol M, Curtin L, Lamb M, Flegal K. Prevalence of high body mass index in us children and adolescents, 2007–2008. J Amer Med Assoc. 2010 Jan;303(3):242–249. [PubMed] 2. Fagot-Campagna A, Saadinem J, Flegal K, Beckles G. Diabetes, impaired fasting glucose, and elevated hba1c in US adolescents: The third national health and nutrition examination survey. Diabetes Care. 2001;24:834–837. [PubMed] 3. Livingstone M, Robson P, Wallace J. Issues in dietary intake assessment of children and adolescents. British J Nutrition. 2004;92:S213–S222. [PubMed] 4. Rockett H, Berkey C, Colditz G. Evaluation of dietary assessment instruments in adolescents. Current Opinion in Clinical Nutrition Metabol Care. 2003;6:557–562. [PubMed]
5. McPherson R, Hoelscher D, Alexander M, Scanlon K, Serdula M. Dietary assessment methods among school-aged children: Validity and reliabality. Preventive Med. 2000;31:S11–S33.
6. Larsson C, Westerterp K, Johansson G. Validity of reported energy expenditure and energy and protein intakes in Swedish adolescent vegans and omnivores. Amer J Clinical Nutrition. 2002;75:268–274. [PubMed] 7. Bandini L, Must A, Cyr H, Anderson S, Spadano J, Dietz W. Longitudinal changes in the accuracy of reported energy intake in girls 10–15 y of age. Amer J Clinical Nutrition. 2003;78:480–484. [PubMed] 8. Six B, Schap T, Zhu F, Mariappan A, Bosch M, Delp E, Ebert D, Kerr D, Boushey C. Evidence-based development of a mobile telephone food record. J Amer Dietetic Assoc. 2010 Jan;:74–79. [PMC free article] [PubMed] 9. Boushey C, Kerr D, Wright J, Lutes K, Ebert D, Delp E. Use of technology in children's dietary assessment. Eur J Clinical Nutrition. 2009:S50–S57. [PMC free article] [PubMed]
10. Zhu F, Mariappan A, Kerr D, Boushey C, Lutes K, Ebert D, Delp E. Technology-assisted dietary assessment. Proc IS&T/SPIE Conf Comput Imaging VI; San Jose, CA. Jan. 2008;
11. Mariappan A, Bosch M, Zhu F, Boushey CJ, Kerr DA, Ebert DS, Delp EJ. Personal dietary assessment using mobile devices. Proc IS&T/SPIE Conf Comput Imaging VII; San Jose, CA. Jan. 2009;
12. Woo I, Otsmo K, Kim S, Ebert DS, Delp EJ, Boushey CJ. Automatic portion estimation and visual refinement in mobile dietary assessment. Proc IS&T/SPIE Conf Comput Imaging VIII; San Jose, CA. Jan. 2010.
13. Klesges R, Eck L, Ray J. Who underreports dietary intake in a dietary recall? Evidence from the second national health and nutrition examination survey. J Consulting Clinical Psychol. 1995;63:438–444. [PubMed] 14. Johnson R, Soultanakis R, Matthews D. Literacy and body fatness are associated with underreporting of energy intake in US low-income women using the multiple-pass 24-hour recall: A doubly labeled water study. J Amer Dietetic Assoc. 1998;98:1136–1140. [PubMed] 15. Tooze J, Subar A, Thompson F, Troiano R, Schatzkin A, Kipnis V. Psychosocial predictors of energy underreporting in a large doubly labeled water study. Amer J Clinical Nutrition. 2004;79:795–804. [PubMed] 16. Bathalon G, Tucker K, Hays N, Vinken A, Greenberg A, McCrory M, Roberts S. Psychological measures of eating behavior and the accuracy of 3 common dietary assessment methods in healthy post-menopausal women. Amer J Clinical Nutrition. 2000;71:739–745. [PubMed] 17. Sawaya A, Tucker K, Tsay R, Willett W, Saltzman E, Dallal G, Roberts S. Evaluation of four methods for determining energy intake in young and older women: Comparison with doubly labeled water measurements of total energy expenditure. Amer J Clinical Nutrition. 1996;63:491–499. [PubMed] 18. Baxter S, Thompson W, Litaker M, Frye F, Guinn C. Low accuracy and low consistency of fourth-graders' school breakfast and school lunch recalls. J Amer Dietetic Assoc. 2002;102:386–395. [PMC free article] [PubMed] 19. Bolland J, Ward J, Bolland T. Improved accuracy of estimating food quantities up to 4 weeks after training. J Amer Dietetic Assoc. 1990;90:1402–1407. [PubMed] 20. Rebro S, Patterson R, Kristal A, Cheney C. The effect of keeping food records on eating patterns. J Amer Dietetic Assoc. 1998;98:1163–1165. [PubMed] 21. Trabulsi J, Schoeller D. Evaluation of dietary assessment instruments against doubly labeled water, a biomarker of habitual energy intake. Amer J Physiol—Endocrinol Metabolism. 2001;281:E891–E899. [PubMed] 22. Champagne C, Baker N, DeLany J, Harsha D, Bray G. Assessment of energy intake underreporting by doubly labeled water and observations on reported nutrient intakes in children. J Amer Dietetic Assoc. 1998;98:426–433. [PubMed] 23. Livingstone M, Black A. Validation of estimates of energy intake by weighed dietary record and diet history in children and adolescents. J Nutrition. 2003;133:S895. –. [PubMed] 24. Weber J, Cunningham-Sabo L, Skipper B, Lytle L, Stevens J, Gittelsohn J, Anliker J, Heller K, Pablo J. Portion-size estimation training in second and third-grade American Indian children. Amer J Clinical Nutrition. 1999;69:782S–787S. [PubMed] 25. Liu K, Stamler J, Dyer A, McKeever J, McKeever P. Statistical methods to assess and minimize the role of intra-individual variability in obscuring the relationship between dietary lipids and serum cholesterol. J Chronic Diseases. 1978;31:399–418. [PubMed] 26. Nelson M, Black A, Morris J, Cole T. Between- and within-subject variation in nutrient intake from infancy to old age: Estimating the number of days required to rank dietary intakes with desired precision. Amer J Clinical Nutrition. 1989;50:155–167. [PubMed]
27. Willett W. Nutritional Epidemiology. New York: Oxford Univ Press; 1998.
28. Beaton G, Milner J, Corey P, McGuire V, Cousins M, Stewart E, deRamos M, Hewitt D, Grambsch P, Kassim N, Little J. Sources of variance in 24-hour dietary recall data: Implications for nutrition study design and interpretation. Amer J Clinical Nutrition. 1979;32:2546–2549. [PubMed]
29. Freudenheim JGE. Biomarkers of nutritional exposure and nutritional status. J Nutrition. 2003;133:871S–973S. Supplement.
30. Black A, Prentice A, Goldberg G, Jebb S, Bingham S, Livingstone M, Coward W. Measurements of total energy expenditure provide insights into the validity of dietary measurements of energy intake. J Amer Dietetic Assoc. 1993;93:572–579. [PubMed] 31. McKeown N, Day N, Welch A, Runswick S, Luben R, Mulligan A, McTaggart A, Bingham S. Use of biological markers to validate self-reported dietary intake in a random sample of the European prospective investigation into cancer United Kingdom Norfolk Cohort. Amer J Clinical Nutrition. 2001;74:188–196. [PubMed] 32. Bingham SA. Urine nitrogen as a biomarker for the validation of dietary protein intake. J Nutrition. 2003;133:921S–924S. [PubMed] 33. Mayne S. Antioxidant nutrients and chronic disease: Use of biomarkers of exposure and oxidative stress status in epidemiologic research. J Nutrition. 2003;133:933S–940S. [PubMed] 34. Townsend M, Kaiser L, Allen L, Joy A, Murphy S. Selecting items for a food behavior checklist for a limited-resource audience. J Nutrition Educat Behavior. 2003;35:69–77. [PubMed] 35. Murphy S, Kaiser L, Townsend M, Allen L. Evaluation of validity of items for a food behavior checklist. J Amer Dietetic Assoc. 2001;101:761. –. [PubMed]
36. Jimenez A, Jain A, Ceres R, Pons J. Automatic fruit recognition: A survey and new results using range/attenuation images. Pattern Recognition. 1999;32:1719–1736.
37. Mery D, Pedreschi F. Segmentation of colour food images using a robust algorithm. J Food Eng. 2005;66:353–360.
38. Kass M, Witkin A, Terzopoulos D. Snakes: Active contour models. Int J Comput Vis. 1998;1:321–331.
39. Caselles V, Kimmel R, Sapiro G. Geodesic active contours. Int J Comput Vis. 1997;22(1):61–79.
40. Malladi R, et al. Shape modeling with front propagation: A level set approach. IEEE Trans Pattern Anal Mach Intell. 1995 Feb;17(2):158–175.
41. Chan T, Vese L. Active contours without edges. IEEE Trans Image Process. 2001 Feb;10(2):266–277. [PubMed] 42. Tsai A, Yezzi A, Willsky A. Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification. IEEE Trans Image Process. 2001 Aug;10(8):1169–1186. [PubMed]
43. Zhu S, Yuille A. Region competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation. IEEE Trans Pattern Anal Mach Intell. 1996 Sep;18(9):884–900.
44. Chan T, Sandberg B, Vese L. Active contours without edges for vector-valued images. J Vis Commun Image Represent. 2000;11(2):130–141.
45. Shi J, Malik J. Normalized cuts and image segmentation. IEEE Tran Pattern Anal Mach Intell. 2000 Aug;22(8):888–905.
46. Kruizinga P, Petkov N, Grigorescu SE. Comparison of texture features based on Gabor filters. Proc 10th Int Conf Image Anal Process; Washington, DC. Sep. 1999; pp. 142–147.
47. Jain A, Farrokhnia F. Unsupervised texture segmentation using Gabor filters. Pattern Recognition. 1991;24(12):1167–1186.
48. Fukunaga K. Introduction to Statistical Pattern Recognition. San Diego, CA: Academic; 1990.
49. Duta R, Hart P, Stork D. Pattern Classification. New York, NY: Wiley-Interscience; 2000.
50. Cristianini N, Taylor J. An Introduction to Support Vector Machines. Cambridge, U.K.: Cambridge Univ Press; 2000.
51. Burges CJC. A tutorial on support vector machines for pattern recognition. Data Mining Knowl Discov. 1998;2(2):121–167.
52. Muller K, Mika S, Ratsch G, Tsuda K, Scholkopf B. An introduction to kernel-based learning algorithms. IEEE Trans Neural Netw. 2001 Mar;12(2):181–201. [PubMed]
54. Usda Food and Nutrient Database for Dietary Studies, 3.0. Beltsville, MD: Agricultural Research Service, Food Surveys Research Group; 2004.
55. Ogilvy C. Excursions in Geometry. New York: Dover; 1990.
56. Dunham W. Journey Through Genius: The Great Theorems of Mathematics. New York: Wiley; 1990.
57. Guibas L, Stolfi J. Primitives for the manipulation of general subdivisions and the computation of Voronoi. ACM Trans Graphics (TOG) 1985;4(2):123–141.
58. Kim S, Woo I, Zhu F, Ostmo K, Boushey CJ, Delp EJ, Ebert D. Interactive and image-based dietary assessment system using mobile devices. Internal Rep, Purdue Univ. available from the authors.