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Bioinspired CNNs for border completion in occluded images

arXiv cs.CV / 3/12/2026

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Key Points

  • The authors design BorderNet, a CNN whose filters are inspired by border-completion mechanisms in the visual cortex to improve robustness to occlusions.
  • They evaluate BorderNet on occluded MNIST, Fashion-MNIST, and EMNIST using stripe and grid occlusions, reporting improved performance over baselines with results that vary by occlusion severity and dataset.
  • The study demonstrates how neuroscience-inspired filter design can enhance occluded-object recognition in CNNs, suggesting potential for more robust vision systems.
  • This is an arXiv preprint (v1) announced as a new contribution in the field.

Abstract

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.

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