| DOI | Trouver le DOI : https://doi.org/10.1109/BIBM52615.2021.9669598 |
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| Auteur | Rechercher : Mozaffari, M. Hamed1; Rechercher : Lee, Won-Sook |
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| Affiliation | - Conseil national de recherches du Canada. Centre de recherche en métrologie
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| Format | Texte, Article |
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| Conférence | 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), December 9-12, 2021, Houston, TX, USA |
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| Sujet | object tracking in ultrasound data; tongue contour extraction in real-time; deep learning for video segmentation; peripheral vision in image processing; semantic segmentation; deep learning; training; image segmentation; ultrasonic imaging; tongue; streaming media; real-time systems |
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| Résumé | The progress of deep convolutional neural networks has been successfully exploited in various real-time computer vision tasks such as image classification and segmentation. Owing to the development of computational units, availability of digital datasets, and improved performance of deep learning models, fully automatic and accurate tracking of tongue contours in real-time ultrasound data became practical only in recent years. Previous studies have shown that the performance of deep learning techniques is significant in tracking ultrasound tongue contours in real-time applications such as pronunciation training using multimodal ultrasound-enhanced approaches. In this paper, we investigated the performance of a novel convolutional neural network inspired by the peripheral vision ability of the human eye (named IrisNet) in tongue contour tracking tasks. Qualitative and quantitative assessment of IrisNet on different ultrasound tongue datasets revealed its outstanding generalization ability of the network compared with similar techniques. |
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| Date de publication | 2021-12-09 |
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| Maison d’édition | IEEE |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 92d6e34d-cddc-4da8-919e-a8b63db8643d |
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| Enregistrement créé | 2023-01-11 |
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| Enregistrement modifié | 2023-01-11 |
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