A Case Study on Master Students with the Textile Background in Tackling Fiber Identification Problems

DOI

10.2991/erms-18.2018.17How to use a DOI?

Keywords

Wool, Cashmere, SEM, SURF, SVM, Deep learning

Abstract

The identification of Wool/Cashmere fibers is an extremely challengeable problem since both fibers possess highly similar morphological features. In this research, we monitored two group of master students with the textile background in tackling this specific task. Both groups have no experience on machine vision. Two learning curves, machine learning, and deep learning were randomly distributed to the groups for them to learn. The entire experimental procedure was carried under the same condition, i.e., computing resources, laboratory facilities, working environment, and weekly discussion with an academic advisor. From our observation, higher accuracy has been reached by the group following the path of deep learning. Comparatively, knowledge of image analysis or hand-crafted feature extraction has empowered the students following the path of machine learning with a more fundamental understanding of feature extraction.

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  - Yue-qi Zhong
PY  - 2018/04
DA  - 2018/04
TI  - A Case Study on Master Students with the Textile Background in Tackling Fiber Identification Problems
BT  - Proceedings of the 2018 International Conference on Education Reform and Management Science (ERMS 2018)
PB  - Atlantis Press
SP  - 76
EP  - 80
SN  - 2352-5398
UR  - https://doi.org/10.2991/erms-18.2018.17
DO  - 10.2991/erms-18.2018.17
ID  - Zhong2018/04
ER  -