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Hardware based scale- and rotation-invariant feature extraction: A retrospective analysis and future directions

Ehsan, S and Clark, AF and McDonald-Maier, KD (2009) Hardware based scale- and rotation-invariant feature extraction: A retrospective analysis and future directions. In: UNSPECIFIED, ? - ?.

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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. © 2009 IEEE.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: 2009 International Conference on Computer and Electrical Engineering, ICCEE 2009
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 15 Aug 2012 14:11
Last Modified: 05 Feb 2019 19:15
URI: http://repository.essex.ac.uk/id/eprint/2277

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