A Fabric Defect Classification Based on Two-dimensional Sparse Representations and a Norm Optimization

DOI

10.2991/icemc-16.2016.34How to use a DOI?

Keywords

Two-dimensional sparse; Fabric defects; Norm optimization; Classification

Abstract

Sampling loss of the structural information of the image for the one-dimensional compression and bring about the loss of recognition accuracy, we propose the concept of two-dimensional compression samples. Using a set of sparse-based perception to get the sparse data on the raw data of the defect, fabric defect two-dimensional sparse. Finally, use of norm optimization method accurately decrypt the compressed data, the eigenvalues of different fabric defect classification. This approach solves the proliferation of data collection and the sensor waste greatly reduces the computational complexity, fabric defect classification, and thus to lay a theoretical foundation for machine vision to identify fabric defects.

Copyright

© 2016, 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  - Yuanshao Hou
AU  - Jiande Fan
PY  - 2016/05
DA  - 2016/05
TI  - A Fabric Defect Classification Based on Two-dimensional Sparse Representations and a Norm Optimization
BT  - Proceedings of the 2016 International Conference on Education, Management and Computer Science
PB  - Atlantis Press
SP  - 166
EP  - 171
SN  - 1951-6851
UR  - https://doi.org/10.2991/icemc-16.2016.34
DO  - 10.2991/icemc-16.2016.34
ID  - Hou2016/05
ER  -