Alwaely, B. and Abhayaratne, C. orcid.org/0000-0002-2799-7395 (2019) Adaptive graph formulation for 3D shape representation. In: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). ICASSP 2019, 12-17 May 2019, Brighton, United Kingdom. IEEE , pp. 1947-1951. ISBN 978-1-4799-8131-1
Abstract
3D shape recognition has attracted a great interest in computer vision due to its large number of important and exciting applications. This has led to exploring a variety of approaches to develop more efficient 3D analysis methods. However, current works take into account descriptions of global shape to generate models, ignoring small differences causing the problem of mismatching, especially for high similarity shapes. The present paper, therefore, proposes a new approach to represent 3D shapes based on graph formulation and its spectral analysis which can accurately represent local details and small surface variations. An adaptive graph is generated over the 3D shape to characterise the topology of the shape, followed by extracting a set of discriminating features to characterise the shape structure to train a classifier. The evaluation results show that the proposed method exceeds the state-of-the-art performance by 4% for a challenging dataset.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2019 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: | 3D shape representation; Graph theory; Connectivity; Growing Neural Gas (GNG); Graph spectral analysis |
Dates: |
|
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: | 10 May 2019 15:20 |
Last Modified: | 17 Apr 2020 00:39 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/icassp.2019.8682859 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:145960 |