  Computer Vision techniques represent a class of algorithms that are highly
computation and data intensive in nature. Generally, performance of these
algorithms in terms of execution speed on desktop computers is far from
real-time. Since real-time performance is desirable in many applications,
special-purpose hardware is required in most cases to achieve this goal. Scale-
and rotation-invariant local feature extraction is a low level computer vision
task with very high computational complexity. The state-of-the-art algorithms
that currently exist in this domain, like SIFT and SURF, suffer from slow
execution speeds and at best can only achieve rates of 2-3 Hz on modern desktop
computers. Hardware-based scale- and rotation-invariant local feature
extraction is an emerging trend enabling real-time performance for these
computationally complex algorithms. This paper takes a retrospective look at
the advances made so far in this field, discusses the hardware design
strategies employed and results achieved, identifies current research gaps and
suggests future research directions.
