Deep ContourFlow: Advancing Active Contours with Deep Learning
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911955815497728 |
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| author | Habis, Antoine Meas-Yedid, Vannary Angelini, Elsa Olivo-Marin, Jean-Christophe |
| author_facet | Habis, Antoine Meas-Yedid, Vannary Angelini, Elsa Olivo-Marin, Jean-Christophe |
| contents | This paper introduces a novel approach that combines unsupervised active contour models with deep learning for robust and adaptive image segmentation. Indeed, traditional active contours, provide a flexible framework for contour evolution and learning offers the capacity to learn intricate features and patterns directly from raw data. Our proposed methodology leverages the strengths of both paradigms, presenting a framework for both unsupervised and one-shot approaches for image segmentation. It is capable of capturing complex object boundaries without the need for extensive labeled training data. This is particularly required in histology, a field facing a significant shortage of annotations due to the challenging and time-consuming nature of the annotation process. We illustrate and compare our results to state of the art methods on a histology dataset and show significant improvements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_10696 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep ContourFlow: Advancing Active Contours with Deep Learning Habis, Antoine Meas-Yedid, Vannary Angelini, Elsa Olivo-Marin, Jean-Christophe Computer Vision and Pattern Recognition This paper introduces a novel approach that combines unsupervised active contour models with deep learning for robust and adaptive image segmentation. Indeed, traditional active contours, provide a flexible framework for contour evolution and learning offers the capacity to learn intricate features and patterns directly from raw data. Our proposed methodology leverages the strengths of both paradigms, presenting a framework for both unsupervised and one-shot approaches for image segmentation. It is capable of capturing complex object boundaries without the need for extensive labeled training data. This is particularly required in histology, a field facing a significant shortage of annotations due to the challenging and time-consuming nature of the annotation process. We illustrate and compare our results to state of the art methods on a histology dataset and show significant improvements. |
| title | Deep ContourFlow: Advancing Active Contours with Deep Learning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.10696 |