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Exploring Integral Image Word Length Reduction Techniques for SURF Detector

Ehsan, Shoaib and McDonald-Maier, Klaus D (2009) Exploring Integral Image Word Length Reduction Techniques for SURF Detector. In: 2009 Second International Conference on Computer and Electrical Engineering, 2009-12-28 - 2009-12-30.

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Speeded Up Robust Features (SURF) is a state of the art computer vision algorithm that relies on integral image representation for performing fast detection and description of image features that are scale and rotation invariant. Integral image representation, however, has major draw back of large binary word length that leads to substantial increase in memory size. When designing a dedicated hardware to achieve real-time performance for the SURF algorithm, it is imperative to consider the adverse effects of integral image on memory size, bus width and computational resources. With the objective of minimizing hardware resources, this paper presents a novel implementation concept of a reduced word length integral image based SURF detector. It evaluates two existing word length reduction techniques for the particular case of SURF detector and extends one of these to achieve more reduction in word length. This paper also introduces a novel method to achieve integral image word length reduction for SURF detector. © 2009 IEEE.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Notes: ICCEE 2009
Uncontrolled Keywords: cs.CV
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 18 Sep 2013 12:41
Last Modified: 15 Jan 2022 00:24

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