  Humans are continuously exposed to a stream of visual data with a natural
temporal structure. However, most successful computer vision algorithms work at
image level, completely discarding the precious information carried by motion.
In this paper, we claim that processing visual streams naturally leads to
formulate the motion invariance principle, which enables the construction of a
new theory of learning that originates from variational principles, just like
in physics. Such principled approach is well suited for a discussion on a
number of interesting questions that arise in vision, and it offers a
well-posed computational scheme for the discovery of convolutional filters over
the retina. Differently from traditional convolutional networks, which need
massive supervision, the proposed theory offers a truly new scenario for the
unsupervised processing of video signals, where features are extracted in a
multi-layer architecture with motion invariance. While the theory enables the
implementation of novel computer vision systems, it also sheds light on the
role of information-based principles to drive possible biological solutions.
