Handling complex backgrounds and light perturbations for enhancing learning tasks from images of vegetables
Rattachement africain : it, si. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Abstract The quality assessment of fruits and vegetables is crucial in the agroalimentary supply chain, as it directly affects consumer satisfaction, market value and overall food security. Traditional approaches rely on visual inspections or destructive techniques, which are labor-intensive and time-consuming. On the contrary, non-destructive techniques emerged as promising alternatives, offering solutions that can be adopted in real environments. Previous studies emphasized that the color distribution over images plays a significant role in the quality evaluation of food. In this paper, we propose a solution that leverages an autoencoder architecture to extract groups of relevant colors from the complete histogram of colors. To enhance the analysis of real-world images with complex backgrounds, we employ a pre-trained U2-Net architecture for background removal. Moreover, we propose a novel procedure based on outlier detection to identify and remove parts of the background that are not fully eliminated, especially along the edges of the product. After this preprocessing, we extract a complete color histogram which is fed to an autoencoder architecture, to extract high-level features representing color groups at different levels of granularity. The goal is to make the learned models less sensitive to light and color perturbations. Our experiments, conducted on two real-world datasets related to two different learning tasks, demonstrated the effectiveness of the proposed solution, that outperformed several baseline and state-of-the-art approaches, also based on complex neural network architectures.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Handling complex backgrounds and light perturbations for enhancing learning tasks from images of vegetables
- Date Crossref
- 10/10/2025
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Consorzio Interuniversitario Nazionale per l'Informatica pays non établi dans la noticeStructure de recherche
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University of Bari Aldo Moro Dept. of Computer Science pays non établi dans la noticeUniversité ou école supérieure
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Institute of Intelligent Systems for Automation pays non établi dans la noticeStructure de recherche
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National Research Council pays non établi dans la noticeOrganisation à but non lucratif
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Jožef Stefan Institute Dept. of Knowledge Technologies pays non établi dans la noticeStructure de recherche
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National Interuniversity Consortium for Informatics Data Science Lab. pays non établi dans la noticeUniversité ou école supérieure
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Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing pays non établi dans la noticeStructure de recherche
Consorzio Interuniversitario Nazionale per l'Informatica, Dept. of Computer Science — University of Bari Aldo Moro et Institute of Intelligent Systems for Automation, avec 4 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.