Hardware based Scale- and Rotation-Invariant Feature Extraction: A Retrospective Analysis and Future Directions
This addresses the need for real-time performance in computer vision applications by summarizing hardware solutions, but it is incremental as it focuses on retrospective analysis and future directions rather than new breakthroughs.
The paper reviews hardware-based approaches for achieving real-time scale- and rotation-invariant feature extraction, noting that current software methods like SIFT and SURF are limited to 2-3 Hz on desktops, which is insufficient for many applications.
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.