| DOI | Trouver le DOI : https://doi.org/10.23919/FUSION49751.2022.9841256 |
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| Auteur | Rechercher : Debaque, B.; Rechercher : Perreault, H.; Rechercher : Mercier, J.-P.; Rechercher : Drouin, M.-A.1; Rechercher : David, R.; Rechercher : Chatelais, B.; Rechercher : Duclos-Hindie, N.; Rechercher : Roy, S. |
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| Affiliation | - Conseil national de recherches du Canada. Technologies numériques
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| Format | Texte, Article |
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| Conférence | 2022 25th International Conference on Information Fusion (FUSION), Julu 4-7, 2022, Linköping, Sweden |
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| Sujet | training; image registration; computer vision; estimation; machine learning; jitter; feature extraction |
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| Résumé | Fusing thermal and visible images is a recurring challenge in computer vision, especially when the images of the two modalities are not well registered. This registration problem is traditionally solved by matching descriptors and depends on the richness and discriminating power of the representation. Ensuring that detected features are dense and uniformly distributed is not necessarily guaranteed. More recently, machine learning methods addressed the issue of visible to visible matching, but few address the multi-modality setting. In this paper, we propose to address the special case of thermal-visible image registration with small baseline parallax correction. Our deep homography model is evaluated on an open thermal and visible dataset with two training settings, unsupervised and supervised. Results demonstrate the feasibility of the approach, and performances comparison to state-of-the-art models is evaluated. |
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| Date de publication | 2022-07-04 |
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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 | d1396456-42e2-4c9e-b75d-fb8f7b6027b0 |
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| Enregistrement créé | 2022-09-09 |
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| Enregistrement modifié | 2022-09-14 |
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