  Computer vision is one of the most active research fields in information
technology today. Giving machines and robots the ability to see and comprehend
the surrounding world at the speed of sight creates endless potential
applications and opportunities. Feature detection and description algorithms
can be indeed considered as the retina of the eyes of such machines and robots.
However, these algorithms are typically computationally intensive, which
prevents them from achieving the speed of sight real-time performance. In
addition, they differ in their capabilities and some may favor and work better
given a specific type of input compared to others. As such, it is essential to
compactly report their pros and cons as well as their performances and recent
advances. This paper is dedicated to provide a comprehensive overview on the
state-of-the-art and recent advances in feature detection and description
algorithms. Specifically, it starts by overviewing fundamental concepts. It
then compares, reports and discusses their performance and capabilities. The
Maximally Stable Extremal Regions algorithm and the Scale Invariant Feature
Transform algorithms, being two of the best of their type, are selected to
report their recent algorithmic derivatives.
