  In the paper we introduce a novel bipolar morphological neuron and bipolar
morphological layer models. The models use only such operations as addition,
subtraction and maximum inside the neuron and exponent and logarithm as
activation functions for the layer. The proposed models unlike previously
introduced morphological neural networks approximate the classical computations
and show better recognition results. We also propose layer-by-layer approach to
train the bipolar morphological networks, which can be further developed to an
incremental approach for separate neurons to get higher accuracy. Both these
approaches do not require special training algorithms and can use a variety of
gradient descent methods. To demonstrate efficiency of the proposed model we
consider classical convolutional neural networks and convert the pre-trained
convolutional layers to the bipolar morphological layers. Seeing that the
experiments on recognition of MNIST and MRZ symbols show only moderate decrease
of accuracy after conversion and training, bipolar neuron model can provide
faster inference and be very useful in mobile and embedded systems.
