  In the last ten years, Convolutional Neural Networks (CNNs) have formed the
basis of deep-learning architectures for most computer vision tasks. However,
they are not necessarily optimal. For example, mathematical morphology is known
to be better suited to deal with binary images. In this work, we create a
morphological neural network that handles binary inputs and outputs. We propose
their construction inspired by CNNs to formulate layers adapted to such images
by replacing convolutions with erosions and dilations. We give explainable
theoretical results on whether or not the resulting learned networks are indeed
morphological operators. We present promising experimental results designed to
learn basic binary operators, and we have made our code publicly available
online.
