Teeth Classification Based on Haar Wavelet Transform and Support Vector Machine

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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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  -