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Nowadays, people are more interested in their health by maintaining a proper diet. Today’s lifestylecauses obesity and malnutrition in humans because of an uncontrolled diet. This paper proposes thehealth monitoring system using the body mass index (BMI) calorie calculator, which guides people totake proper calories from their daily diet. The image processing steps segmentation, features extraction,and recognition are used in the dietary assessment to identify the food items. The improved performanceof the multi-hypotheses image segmentation (MHS) and feed-forward neural network (FFNN) classifier fornutritional assessment was evaluated using macro average accuracy (MAA) and standard accuracy (SA)metrics, which provide an enhanced classification rate.
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Tom
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177--189
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Bibliogr. 14 poz., rys., tab., wykr.
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Bibliografia
- [1] M. Anthimopoulos, J. Dehais, P. Diem, S. Mougiakakou. Segmentation and recognition of multi-food meal images for carbohydrate counting. In 13th IEEE International Conference on Bioinformatics and Bioengineering, Chania, Greece, pp. 1–4, 2013, doi: 10.1109/BIBE.2013.6701608.
- [2] H. Bay, T. Tuytelaars, L. Van Gool. SURF: Speeded Up Robust Features. [In:] Leonardis A., Bischof H., Pinz A. [Eds], Computer Vision – ECCV 2006. Lecture Notes in Computer Science, vol. 3951, Springer, Berlin, Heidelberg, 2006, doi: /10.1007/1174402332.
- [3] Y. Kawano, K. Yanai. Automatic Expansion of a Food Image Dataset Leveraging Existing Categories with Domain Adaptation. [In:] Proceedings of ECCV Workshop on Transferring and Adapting Source Knowledge in Computer Vision (TASK-CV), pp. 1–16, 2014, doi: 10.1007/978-3-319-16199-01.
- [4] J. Canny. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8 (6): 679–698, 1986, doi: 10.1109/TPAMI.1986.4767851.
- [5] G. Ciocca, P. Napoletano, R. Schettini. Food recognition: a new dataset, experiments, and results. IEEE Journal of Biomedical and Health Informatics, 21 (3): 588–598, 2017, doi: 10.1109/JBHI.2016.2636441.
- [6] W.T. Freeman, E.H. Adelson. The design and use of steerable filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13 (9): 891–906, 1991, doi: 10.1109/34.93808.
- [7] D.G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60 (2): 91–110, 2004, doi: 10.1023/B:VISI.0000029664.99615.94.
- [8] S.J. Minija, W.R.S. Emmanuel. Neural network classifier and multiple hypothesis image segmentation-for dietary assessment using calorie calculator.The Imaging Science Journal,65(7): 379–392, 2017, doi:10.1080/13682199.2017.1356610.
- [9] P. Pouladzadeh, G. Villalobos, R. Almaghrabi, S. Shirmohammadi. A novel SVM based food recognition methodfor calorie measurement applications. [In:]Proceedings of 2012 IEEE International Conference on Multimediaand Expo Workshops (ICMEW), pp. 495–498, 2012.
- [10] M.H. Rahmanet al.Food volume estimation in a mobile phone based dietary assessment system. [In:]2012 8thIEEE International Conference on Signal Image Technology and Internet Based Systems, pp. 988–995, 25–29Nov. 2012, Naples, Italy, doi: 10.1109/SITIS.2012.146.
- [11] K. Saravanan, S. Sasithra. Review on classification based on artificial neural networks.International Journal ofAmbient Systems and Applications,2(4): 11–18, 2014, doi: 10.5121/ijasa.2014.2402.
- [12] B.L. Sixet al.Evidence-based development of a mobile telephone food record.Journal of the American DieteticAssociation,110(1): 74–79, 2010, doi: 10.1016/j.jada.2009.10.010.
- [13] F. Zhu, M. Bosch, N. Khanna, C.J. Boushey, E.J. Delp. Multiple hypotheses image segmentation and classifica-tion with application to dietary assessment.IEEE Journal of Biomedical and Health Informatics,19(1): 377–388,2015, doi: 10.1109/JBHI.2014.2304925.
- [14] A. Biem, S. Katagiri. Feature extraction based on minimum classification error/generalized probabilistic descentmethod. [In:]Proceedings of IEEE International Conference on Acoustic, Speech, Signal Process, pp. 275–278,Apr. 1993, doi: 10.1109/ICASSP.1993.319289.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-f62fe2ac-5792-4b22-83a7-24306c1d0cd2