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Título: | Artificial intelligence to estimate wine volume from single-view images |
Autor: | Cobo Cano, Miriam CSIC ORCID ; Heredia, Ignacio CSIC ORCID; Aguilar, Fernando CSIC ORCID ; Lloret Iglesias, Lara CSIC ORCID; García Díaz, Daniel CSIC ORCID ; Bartolomé, Begoña CSIC ORCID ; Moreno-Arribas, M. Victoria CSIC ORCID ; Yuste, Silvia CSIC ORCID; Pérez-Matute, Patricia; Motilva, María-José CSIC ORCID | Palabras clave: | Deep learning model Quantitative red wine volume estimation Single-view image |
Fecha de publicación: | 2022 | Editor: | Elsevier | Citación: | Heliyon 8(9): e10557 (2022) | Resumen: | In this paper, we present a method to determine the volume of wine in different types of glass liquid containers from a single-view image. The proposed model predicts red wine volume from a photograph of the glass containing the wine. Experimental results demonstrated satisfactory performance of our image-based wine measurement system, with a Mean Absolute Error lower than 10mL. To train and evaluate our system, we introduced the WineGut_BrainUp dataset, a new dataset of glasses of wine that contains 24305 laboratory images, including a wide range of containers, volumes of wine, backgrounds, object distances, angles and lightning, with or without calibration object. The proposed methodology is a suitable analytical tool for automate measurement of red wine volume. Indeed, it has potential real life applications in diet monitoring and wine consumption studies. | Versión del editor: | https://doi.org/10.1016/j.heliyon.2022.e10557 | URI: | http://hdl.handle.net/10261/279920 | DOI: | 10.1016/j.heliyon.2022.e10557 | E-ISSN: | 2405-8440 | Referencias: | Cobo Cano, Miriam; 2023; BrainGut_WineUp tool for wine volume estimation: consumer study validation [Software]; DIGITAL.CSIC; DOI Cobo Cano, Miriam; nov-2021; Liquid volume estimation from RGB images [software]; DIGITAL.CSIC; http://dx.doi.org/10.20350/digitalCSIC/14025 |
Aparece en las colecciones: | (IFCA) Artículos (CIAL) Artículos (ICVV) Artículos |
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