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( 2021 ). Recent Studies on Segmentation Techniques for Food Recognition: A Survey.\u00a0Archives of Computational Methods in Engineering, 1-14 . Chopra, M., & Purwar, A. (2021). Recent Studies on Segmentation Techniques for Food Recognition: A Survey.\u00a0Archives of Computational Methods in Engineering, 1-14."},{"key":"e_1_3_2_1_2_1","volume-title":"The development of food image detection and recognition model of Korean food for mobile dietary management.\u00a0Nutrition research and practice,\u00a013(6), 521-528","author":"Park S. J.","year":"2019","unstructured":"Park , S. J. , Palvanov , A. , Lee , C. H. , Jeong , N. , Cho , Y. I. , & Lee , H. J. ( 2019 ). The development of food image detection and recognition model of Korean food for mobile dietary management.\u00a0Nutrition research and practice,\u00a013(6), 521-528 . Park, S. J., Palvanov, A., Lee, C. H., Jeong, N., Cho, Y. I., & Lee, H. J. (2019). 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Xu, Hang, \"Reasoning-rcnn: Unifying adaptive global reasoning into large-scale object detection.\"\u00a0Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2019."},{"key":"e_1_3_2_1_4_1","volume-title":"with random forests.\"\u00a0European conference on computer vision","author":"Bossard","year":"2014","unstructured":"Bossard , Lukas, Matthieu Guillaumin :, and Luc Van Gool . \"Food-101\u2013mining discriminative ,components , with random forests.\"\u00a0European conference on computer vision . Springer , Cham , 2014 . Bossard, Lukas, Matthieu Guillaumin:, and Luc Van Gool. \"Food-101\u2013mining discriminative ,components, with random forests.\"\u00a0European conference on computer vision. Springer, Cham, 2014."},{"key":"e_1_3_2_1_5_1","volume-title":"and Keiji Yanai. \"Food image recognition with deep convolutional features.\"\u00a0Proceedings of the 2014 ACM - International Joint Conference on Pervasive and Ubiquitous Computing:- Adjunct Publication","author":"Kawano","year":"2014","unstructured":"Kawano , Yoshiyuki :, and Keiji Yanai. \"Food image recognition with deep convolutional features.\"\u00a0Proceedings of the 2014 ACM - International Joint Conference on Pervasive and Ubiquitous Computing:- Adjunct Publication : 2014 . Kawano, Yoshiyuki:, and Keiji Yanai. \"Food image recognition with deep convolutional features.\"\u00a0Proceedings of the 2014 ACM - International Joint Conference on Pervasive and Ubiquitous Computing:- Adjunct Publication: 2014."},{"key":"e_1_3_2_1_6_1","volume-title":"Conf. on Autonomic and Trusted Computing and 2015 IEEE 15th Intl. Conf. on Scalable Computing and Communications and Its Associated Workshops (UIC:-ATC-ScalCom). IEEE","author":"Zhang","year":"2015","unstructured":"Zhang , Weishan, \" Food image recognition with convolutional neural networks.\"\u00a02015 IEEE 12th Intl Conf. on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl . Conf. on Autonomic and Trusted Computing and 2015 IEEE 15th Intl. Conf. on Scalable Computing and Communications and Its Associated Workshops (UIC:-ATC-ScalCom). IEEE , 2015 . Zhang, Weishan, \"Food image recognition with convolutional neural networks.\"\u00a02015 IEEE 12th Intl Conf. on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl. Conf. on Autonomic and Trusted Computing and 2015 IEEE 15th Intl. Conf. on Scalable Computing and Communications and Its Associated Workshops (UIC:-ATC-ScalCom). IEEE, 2015."},{"key":"e_1_3_2_1_7_1","volume-title":"448-453","author":"Shen","year":"2020","unstructured":"Shen , Zhidong, \" Machine learning based approach on food recognition and nutrition estimation.\"\u00a0Procedia Computer Science\u00a0174 ( 2020 ): 448-453 . Shen, Zhidong, \"Machine learning based approach on food recognition and nutrition estimation.\"\u00a0Procedia Computer Science\u00a0174 (2020): 448-453."},{"key":"e_1_3_2_1_8_1","volume-title":"Springer","author":"Kagaya","year":"2015","unstructured":"Kagaya , Hokuto, and Kiyoharu Aizawa . \"Highly accurate food\/non-food image classification based on a deep convolutional neural network.\"\u00a0International conference on image analysis and processing . Springer , Cham , 2015 . Kagaya, Hokuto, and Kiyoharu Aizawa. \"Highly accurate food\/non-food image classification based on a deep convolutional neural network.\"\u00a0International conference on image analysis and processing. 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Kawano, Y., & Yanai, K. (2015). Foodcam: A real-time food recognition system on a smartphone.\u00a0Multimedia Tools and Applications,\u00a074(14), 5263-5287."},{"key":"e_1_3_2_1_11_1","volume-title":"Deep learning-based food image recognition for computer-aided dietary assessment.\"\u00a0International Conference on Smart Homes and Health Telematics","author":"Liu","year":"2016","unstructured":"Liu , Chang, \" Deepfood : Deep learning-based food image recognition for computer-aided dietary assessment.\"\u00a0International Conference on Smart Homes and Health Telematics . Springer , Cham , 2016 . Liu, Chang, \"Deepfood: Deep learning-based food image recognition for computer-aided dietary assessment.\"\u00a0International Conference on Smart Homes and Health Telematics. 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