  The remarkable progress in computer vision over the last few years is, by and
large, attributed to deep learning, fueled by the availability of huge sets of
labeled data, and paired with the explosive growth of the GPU paradigm. While
subscribing to this view, this book criticizes the supposed scientific progress
in the field and proposes the investigation of vision within the framework of
information-based laws of nature. Specifically, the present work poses
fundamental questions about vision that remain far from understood, leading the
reader on a journey populated by novel challenges resonating with the
foundations of machine learning. The central thesis is that for a deeper
understanding of visual computational processes, it is necessary to look beyond
the applications of general purpose machine learning algorithms and focus
instead on appropriate learning theories that take into account the
spatiotemporal nature of the visual signal.
