  The recent impressive results of deep learning-based methods on computer
vision applications brought fresh air to the research and industrial community.
This success is mainly due to the process that allows those methods to learn
data-driven features, generally based upon linear operations. However, in some
scenarios, such operations do not have a good performance because of their
inherited process that blurs edges, losing notions of corners, borders, and
geometry of objects. Overcoming this, non-linear operations, such as
morphological ones, may preserve such properties of the objects, being
preferable and even state-of-the-art in some applications. Encouraged by this,
in this work, we propose a novel network, called Deep Morphological Network
(DeepMorphNet), capable of doing non-linear morphological operations while
performing the feature learning process by optimizing the structuring elements.
The DeepMorphNets can be trained and optimized end-to-end using traditional
existing techniques commonly employed in the training of deep learning
approaches. A systematic evaluation of the proposed algorithm is conducted
using two synthetic and two traditional image classification datasets. Results
show that the proposed DeepMorphNets is a promising technique that can learn
distinct features when compared to the ones learned by current deep learning
methods.
