Bioinspired CNNs for border completion in occluded images
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
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| _version_ | 1866914386308759552 |
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| author | Coutinho, Catarina P. Merhab, Aneeqa Petkovic, Janko Zanchetta, Ferdinando Fioresi, Rita |
| author_facet | Coutinho, Catarina P. Merhab, Aneeqa Petkovic, Janko Zanchetta, Ferdinando Fioresi, Rita |
| contents | We exploit the mathematical modeling of the border completion problem in the visual cortex to design convolutional neural network (CNN) filters that enhance robustness to image occlusions. We evaluate our CNN architecture, BorderNet, on three occluded datasets (MNIST, Fashion-MNIST, and EMNIST) under two types of occlusions: stripes and grids. In all cases, BorderNet demonstrates improved performance, with gains varying depending on the severity of the occlusions and the dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_10694 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Bioinspired CNNs for border completion in occluded images Coutinho, Catarina P. Merhab, Aneeqa Petkovic, Janko Zanchetta, Ferdinando Fioresi, Rita Computer Vision and Pattern Recognition We exploit the mathematical modeling of the border completion problem in the visual cortex to design convolutional neural network (CNN) filters that enhance robustness to image occlusions. We evaluate our CNN architecture, BorderNet, on three occluded datasets (MNIST, Fashion-MNIST, and EMNIST) under two types of occlusions: stripes and grids. In all cases, BorderNet demonstrates improved performance, with gains varying depending on the severity of the occlusions and the dataset. |
| title | Bioinspired CNNs for border completion in occluded images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.10694 |