Recently, Computer Vision based image analysis techniques have attracted a lot of attention because they are used to develop automatic dietary monitoring applications. Food recognition is a quite challenging task: it is a non-rigid object, and is characterized by intrinsic high iter- and intra-class variability. The proper design of a food recognition system based on Computer Vision should contain several analysis stages. This paper reports on the most recent solutions in the field of automatic food recognition using computer vision developed at the Imaging and Vision Laboratory in the last 12 years. We present and discuss the main solutions developed and results achieved for food localization, segmentation, recognition and analysis. Food localization and segmentation aim at identifying the regions in the image corresponding to food items, food recognition aims at labeling each food region with the identity of the depicted food, and food analysis aims at determining properties of the food such as its quantity or ingredients.

Buzzelli, M., Ciocca, G., Napoletano, P., Schettini, R. (2021). Analyzing and Recognizing Food in Constrained and Unconstrained Environments. In AI & Food'21: Proceedings of the 3rd Workshop on AIxFood (pp.1-5). Association for Computing Machinery, Inc [10.1145/3475725.3483624].

Analyzing and Recognizing Food in Constrained and Unconstrained Environments

Buzzelli, Marco;Ciocca, Gianluigi;Napoletano, Paolo;Schettini, Raimondo
2021

Abstract

Recently, Computer Vision based image analysis techniques have attracted a lot of attention because they are used to develop automatic dietary monitoring applications. Food recognition is a quite challenging task: it is a non-rigid object, and is characterized by intrinsic high iter- and intra-class variability. The proper design of a food recognition system based on Computer Vision should contain several analysis stages. This paper reports on the most recent solutions in the field of automatic food recognition using computer vision developed at the Imaging and Vision Laboratory in the last 12 years. We present and discuss the main solutions developed and results achieved for food localization, segmentation, recognition and analysis. Food localization and segmentation aim at identifying the regions in the image corresponding to food items, food recognition aims at labeling each food region with the identity of the depicted food, and food analysis aims at determining properties of the food such as its quantity or ingredients.
No
paper
Scientifica
artificial intelligence; computer vision; convolutional neural networks; food recognition; grocery product recognition.; machine learning; semantic segmentation;
English
3rd Workshop on AIxFood, AIxFood 2021, co-located with ACM MM 2021
9781450386739
Buzzelli, M., Ciocca, G., Napoletano, P., Schettini, R. (2021). Analyzing and Recognizing Food in Constrained and Unconstrained Environments. In AI & Food'21: Proceedings of the 3rd Workshop on AIxFood (pp.1-5). Association for Computing Machinery, Inc [10.1145/3475725.3483624].
Buzzelli, M; Ciocca, G; Napoletano, P; Schettini, R
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/332044
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