Proceedings of the 2018 International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018)

Teeth Classification Based on Haar Wavelet Transform and Support Vector Machine

Authors
Fangyuan Liu, Zhi Li, Wagner Quinn
Corresponding Author
Fangyuan Liu
Available Online February 2018.
DOI
10.2991/csece-18.2018.53How to use a DOI?
Keywords
Keyword—Haar wavelet transform; support vector machine; principal component analysis
Abstract

To improve the efficiency of stomatology practitioners, this paper proposed a novel teeth type classification method. Our method was based on three successful components: Haar wavelet transform, principal component analysis, and support vector machine. We create a 120-image dataset, with 30 images for incisor, canine, premolar, and molar. The results showed our method achieved an overall classification accuracy of 81.83± 1.79%, better than decision tree and multilayer perceptron methods.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2018 International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018)
Series
Advances in Computer Science Research
Publication Date
February 2018
ISBN
10.2991/csece-18.2018.53
ISSN
2352-538X
DOI
10.2991/csece-18.2018.53How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Fangyuan Liu
AU  - Zhi Li
AU  - Wagner Quinn
PY  - 2018/02
DA  - 2018/02
TI  - Teeth Classification Based on Haar Wavelet Transform and Support Vector Machine
BT  - Proceedings of the 2018 International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018)
PB  - Atlantis Press
SP  - 249
EP  - 252
SN  - 2352-538X
UR  - https://doi.org/10.2991/csece-18.2018.53
DO  - 10.2991/csece-18.2018.53
ID  - Liu2018/02
ER  -