A Fabric Defect Classification Based on Two-dimensional Sparse Representations and a Norm Optimization
Yuanshao Hou, Jiande Fan
Available Online May 2016.
- https://doi.org/10.2991/icemc-16.2016.34How to use a DOI?
- Two-dimensional sparse; Fabric defects; Norm optimization; Classification
- 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.
- 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 - 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 - https://doi.org/10.2991/icemc-16.2016.34 ID - Hou2016/05 ER -