최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기Sensors, v.19 no.3, 2019년, pp.564 -
Makhsous, Sepehr (Sensors, Energy, and Automation Laboratory (SEAL), Department of Electrical and Computer Engineering University of Washington, Seattle, WA 98109, USA) , Mohammad, Hashem M. (hashemm@uw.edu (H.M.M.)) , Schenk, Jeannette M. (mamishev@uw.edu (A.V.M.)) , Mamishev, Alexander V. (Sensors, Energy, and Automation Laboratory (SEAL), Department of Electrical and Computer Engineering University of Washington, Seattle, WA 98109, USA) , Kristal, Alan R. (hashemm@uw.edu (H.M.M.))
Over the past ten years, diabetes has rapidly become more prevalent in all age demographics and especially in children. Improved dietary assessment techniques are necessary for epidemiological studies that investigate the relationship between diet and disease. Current nutritional research is hindere...
1. O’Loughlin G. Cullen S.J. McGoldrick A. O’Connor S. Blain R. O’Malley S. Warrington G.D. Using a Wearable Camera to Increase the Accuracy of Dietary Analysis Am. J. Prev. Med. 2013 44 297 301 10.1016/j.amepre.2012.11.007 23415128
2. Kohlmeier L. Gaps in dietary assessment methodology: Meal- vs list-based methods Am. J. Clin. Nutr. 1994 59 175S 179S 10.1093/ajcn/59.1.175S 8279419
3. Schulze M.B. Liu S. Rimm E.B. Manson J.E. Willett W.C. Hu F.B. Glycemic index, glycemic load, and dietary fiber intake and incidence of type 2 diabetes in younger and middle-aged women Am. J. Clin. Nutr. 2004 80 348 356 10.1093/ajcn/80.2.348 15277155
4. Shim J.-S. Oh K. Kim H.C. Dietary assessment methods in epidemiologic studies Epidemiol. Health 2014 36 e2014009 10.4178/epih/e2014009 25078382
5. Martin C.K. Correa J.B. Han H. Allen H.R. Rood J.C. Champagne C.M. Gunturk B.K. Bray G.A. Validity of the Remote Food Photography Method (Rfpm) for Estimating Energy and Nutrient Intake in near Real-Time Obesity 2012 20 891 899 10.1038/oby.2011.344 22134199
6. Harborview Medical Center, N.D.I.D. Meal Measurement Accuracy Done by Staff and Nurses Harborview Medical Center, University of Washington Seattle, WA, USA 2017
7. Chen H.-C. Jia W. Sun X. Li Z. Li Y. Fernstrom J.D. Burke L.E. Baranowski T. Sun M. Saliency-aware food image segmentation for personal dietary assessment using a wearable computer Meas. Sci. Technol. 2015 26 025702 10.1088/0957-0233/26/2/025702 26257473
8. Kong F. He H. Raynor H.A. Tan J. DietCam: Multi-view regular shape food recognition with a camera phone Pervasive Mob. Comput. 2015 19 108 121 10.1016/j.pmcj.2014.05.012
9. Harray A.J. Boushey C.J. Pollard C.M. Delp E.J. Ahmad Z. Dhaliwal S.S. Mukhtar S.A. Kerr D.A. A Novel Dietary Assessment Method to Measure a Healthy and Sustainable Diet Using the Mobile Food Record: Protocol and Methodology Nutrients 2015 7 5375 5395 10.3390/nu7075226 26151176
10. Chan T. Lichti D. Jahraus A. Esfandiari H. Lahamy H. Steward J. Glanzer M. An Egg Volume Measurement System Based on the Microsoft Kinect Sensors 2018 18 2454 10.3390/s18082454 30060589
11. Oliveira L. Nunes U. Peixoto P. Silva M. Moita F. Semantic fusion of laser and vision in pedestrian detection Pattern Recognit. 2010 43 3648 3659 10.1016/j.patcog.2010.05.014
12. Zhang G. Liu Z. Sun J. Wei Z. Novel calibration method for a multi-sensor visual measurement system based on structured light SPIE 2010 49 12 10.1117/1.3407429
13. Ballagas R. Borchers J. Rohs M. Sheridan J.G. The Smart Phone: A Ubiquitous Input Device Pervasive Comput. IEEE 2006 5 70 77 10.1109/MPRV.2006.18
14. Shang J. Duong M. Pepin E. Zhang X. Sandara-Rajan K. Mamishev A. Kristal A. A mobile structured light system for food volume estimation Proceedings of the 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops) Barcelona, Spain 7 November 2011 100 101
15. Minnesota U.O. Nutrition Data System for Research—Nutritional Analysis Software—ndsr87072 University of Minnesota Office for Technology Commercialization Minneapolis, MN, USA 2016
16. Mathworks MATLAB—MathWorks Available online: https://www.mathworks.com/products/matlab.html (accessed on 31 October 2018)
17. Primer D.A. 24-hour Dietary Recall (24HR) at a Glance 2018 Available online: https://dietassessmentprimer.cancer.gov/profiles/recall/ (accessed on 10 November2018)
18. Chen H.-C. Jia W. Yue Y. Li Z. Sun Y.-N. Fernstrom J.D. Sun M. Model-based measurement of food portion size for image-based dietary assessment using 3D/2D registration Meas. Sci. Technol. 2013 24 105701 10.1088/0957-0233/24/10/105701 24223474
19. Kong F. Tan J. DietCam: Automatic dietary assessment with mobile camera phones Pervasive Mob. Comput. 2012 8 147 163 10.1016/j.pmcj.2011.07.003
20. Chae J. Woo I. Kim S. Maciejewski R. Zhu F. Delp E.J. Boushey C.J. Ebert D.S. Volume estimation using food specific shape templates in mobile image-based dietary assessment SPIE 2011 7873 8
21. Hu Y. Wang L. Xiang L. Wu Q. Jiang H. Automatic Non-Destructive Growth Measurement of Leafy Vegetables Based on Kinect Sensors 2018 18 806 10.3390/s18030806 29518958
22. Occipital Structure Sensor 3D Scanner 2018 Available online: https://occipital.com/ (accessed on 3 December 2018)
23. Bazargani H. Laganiere R. Camera calibration and pose estimation from planes IEEE Instrum. Meas. Mag. 2015 18 20 27 10.1109/MIM.2015.7335834
24. D. Systèmes, “3D CAD Design Software,” Getting Started|SOLIDWORKS Available online: https://www.solidworks.com/ (accessed on 29 November 2018)
25. Laser C. Green FLEXPOINT ® 532 nm—FLEXPOINT ® Dot and Line Lasers Laser Components GmbH Olching, Germany 2015
26. Rodríguez J.A.M. Mejía Alanís F.C. Binocular self-calibration performed via adaptive genetic algorithm based on laser line imaging J. Mod. Opt. 2016 63 1219 1232 10.1080/09500340.2015.1130271
27. Hu Z. Li Y. Li N. Zhao B. Extrinsic Calibration of 2-D Laser Rangefinder and Camera from Single Shot Based on Minimal Solution IEEE Trans. Instrum. Meas. 2016 65 915 929 10.1109/TIM.2016.2518248
28. Wang G. Wang Y. Li H. Chen X. Lu H. Ma Y. Peng C. Wang Y. Tang L. Morphological Background Detection and Illumination Normalization of Text Image with Poor Lighting PLoS ONE 2014 9 e110991 10.1371/journal.pone.0110991 25426639
29. MyFitnessPal Lose Weight with MyFitnessPal 2018 Available online: http://www.myfitnesspal.com (accessed on 31 October 2018)
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
오픈액세스 학술지에 출판된 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.