Paper Info
Title
Hardware based Scale- and Rotation-Invariant Feature Extraction: A Retrospective Analysis and Future Directions.
Abstract
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.
Year
DOI
Venue
2015
10.1109/ICCEE.2009.137
ICCEE '09 Proceedings of the 2009 Second International Conference on Computer and Electrical Engineering - Volume 02
Keywords
Field
DocType
computational complexity,computer vision,feature extraction,computational complexity,computer vision task,data intensive,desktop computers,hardware design,rotation-invariant feature extraction,scale-invariant feature extraction
Scale-invariant feature transform,Invariant feature,Algorithm design,Computer science,Field-programmable gate array,Feature extraction,Computer hardware,Computational complexity theory,Computation
Journal
Volume
Citations 
PageRank 
abs/1504.07962
1
0.38
References 
Authors
15
3
Name
Order
Citations
PageRank
Shoaib Ehsan111024.43
Adrian F. Clark222172.99
Klaus D. McDonald-Maier332754.43