Alwaely, B. and Abhayaratne, C. orcid.org/0000-0002-2799-7395 (2017) Graph spectral domain feature representation for in-air drawn number recognition. In: 2017 25th European Signal Processing Conference (EUSIPCO). 25th European Signal Processing Conference (EUSIPCO), 28 Aug - 02 Sep 2017, Kos, Greece. IEEE , pp. 370-374. ISBN 978-0-9928626-7-1
Abstract
The emerging field of graph signal processing has brought new scope in understanding the spectral properties of arbitrary structures. This paper proposes a novel graph spectral domain feature representation scheme for recognising in-air drawn numbers. It provides the solution by forming the hand's path as a graph and extracting its features based on the spectral domain representation by computing the graph spectral transform. A novel graph generation model is proposed to form the topology of the shapes of numbers. The experiments show that the proposed features are flip and rotation-invariant which makes insensitive to changes in the rotation angle of the drawn numbers. The proposed solution achieves a high level of accuracy of nearly 98% for in-air hand drawn number recognition.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | feature extraction; graph theory; image representation; spectral analysis; topology |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 22 Aug 2018 08:55 |
Last Modified: | 19 Dec 2022 13:50 |
Published Version: | https://doi.org/10.23919/EUSIPCO.2017.8081231 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.23919/EUSIPCO.2017.8081231 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:134714 |