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Fast low-level multi-scale feature extraction for hexagonal images

Coleman, SA, Bryan, Scotney and Gardiner, Bryan (2017) Fast low-level multi-scale feature extraction for hexagonal images. In: 2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA), Japan. IEEE. 4 pp. [Conference contribution]

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DOI: 10.23919/MVA.2017.7986871

Abstract

Inspired by the human vision system and its capability to process in real-time, an efficient framework for low-level feature extraction on hexagonal pixel-based images is presented. This is achieved by utilizing the spiral architecture addressing scheme to simulate eye-tremor along with the convolution of non-overlapping gradient masks. Using sparse spiral convolution and the development of cluster operators, we obtain a set of output image responses “a-trous” that is subsequently collated into a consolidated output response; it is also demonstrated that this framework can be extended to feature extraction at different scales. We show that the proposed framework is considerably faster than using conventional spiral convolution or the use of look-up tables for direct access to hexagonal pixel neighbourhood addresses.

Item Type:Conference contribution (Poster)
Keywords:Spirals, Convolution, Machine vision, Feature extraction, Indexes, Computer architecture, Organisations
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Information Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
Computer Science Research Institute > Information and Communication Engineering
ID Code:38904
Deposited By: Dr Bryan Gardiner
Deposited On:30 Oct 2017 10:20
Last Modified:30 Oct 2017 10:20

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