🤖 AI Summary
This study addresses the degradation in recognition performance caused by occlusion-induced loss of boundary information in images. Inspired by the boundary completion mechanism in the visual cortex, this work proposes BorderNet, a novel convolutional neural network architecture that, for the first time, integrates a mathematical model of this biological mechanism into deep learning. By incorporating biologically inspired convolutional filters and an occlusion-robust training strategy, BorderNet effectively enhances the model’s ability to perceive and reconstruct missing boundaries. Experimental results on three occluded benchmark datasets—MNIST, Fashion-MNIST, and EMNIST—demonstrate that BorderNet significantly outperforms existing baseline methods, with particularly notable improvements in classification accuracy under severe occlusion conditions.
📝 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.