Proceedings of the 2016 International Conference on Education, Management and Computer Science

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

Authors
Yuanshao Hou, Jiande Fan
Corresponding Author
Yuanshao Hou
Available Online May 2016.
DOI
https://doi.org/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.
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Proceedings
2016 International Conference on Education, Management and Computer Science
Part of series
Advances in Intelligent Systems Research
Publication Date
May 2016
ISBN
978-94-6252-202-2
ISSN
1951-6851
DOI
https://doi.org/10.2991/icemc-16.2016.34How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

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  - 2016 International Conference on Education, Management and Computer Science
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/icemc-16.2016.34
DO  - https://doi.org/10.2991/icemc-16.2016.34
ID  - Hou2016/05
ER  -