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. (International Conference on Computer and Electrical Engineering ICCEE) IEEE, pp. 635-639. (doi:10.1109/ICCEE.2009.138).
Abstract
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.
This record has no associated files available for download.
More information
Identifiers
Catalogue record
Export record
Altmetrics
Contributors
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.