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- Publisher Website: 10.1137/17M1113138
- Scopus: eid_2-s2.0-85039743959
- WOS: WOS:000418654000016
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Article: Lattice-based patterned fabric inspection by using total variation with sparsity and low-rank representations
Title | Lattice-based patterned fabric inspection by using total variation with sparsity and low-rank representations |
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Authors | |
Keywords | Low-rank Lattice Convex programming Sparsity Patterned fabric inspection Motif |
Issue Date | 2017 |
Citation | SIAM Journal on Imaging Sciences, 2017, v. 10, n. 4, p. 2140-2164 How to Cite? |
Abstract | © 2017 Society for Industrial and Applied Mathematics. In this paper, we study an image decomposition model for patterned fabric inspection. It is important to represent fabric patterns effectively so that fabric defects can be separated. One concern is that both patterned fabric (e.g., star- or box-patterned fabrics) and fabric defects contain mainly low frequency components. The main idea of this paper is to use the convolution of a lattice with a Dirac comb to characterize a patterned fabric image so that its repetitive components can be effectively represented in the image decomposition model. We formulate a model with total variation, sparsity, and low-rank terms for patterned fabric inspection. The total variation term is used to regularize the defective image, and the sparsity and the low-rank terms are employed to control the Dirac comb function. The proposed model can be solved efficiently via a convex programming solver. Our experimental results for different types of patterned fabrics show that the proposed model can inspect defects at a higher accuracy compared with some classical methods in the literature. |
Persistent Identifier | http://hdl.handle.net/10722/277086 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Ng, Michael K. | - |
dc.contributor.author | Ngan, Henry Y.T. | - |
dc.contributor.author | Yuan, Xiaoming | - |
dc.contributor.author | Zhang, Wenxing | - |
dc.date.accessioned | 2019-09-18T08:35:33Z | - |
dc.date.available | 2019-09-18T08:35:33Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | SIAM Journal on Imaging Sciences, 2017, v. 10, n. 4, p. 2140-2164 | - |
dc.identifier.uri | http://hdl.handle.net/10722/277086 | - |
dc.description.abstract | © 2017 Society for Industrial and Applied Mathematics. In this paper, we study an image decomposition model for patterned fabric inspection. It is important to represent fabric patterns effectively so that fabric defects can be separated. One concern is that both patterned fabric (e.g., star- or box-patterned fabrics) and fabric defects contain mainly low frequency components. The main idea of this paper is to use the convolution of a lattice with a Dirac comb to characterize a patterned fabric image so that its repetitive components can be effectively represented in the image decomposition model. We formulate a model with total variation, sparsity, and low-rank terms for patterned fabric inspection. The total variation term is used to regularize the defective image, and the sparsity and the low-rank terms are employed to control the Dirac comb function. The proposed model can be solved efficiently via a convex programming solver. Our experimental results for different types of patterned fabrics show that the proposed model can inspect defects at a higher accuracy compared with some classical methods in the literature. | - |
dc.language | eng | - |
dc.relation.ispartof | SIAM Journal on Imaging Sciences | - |
dc.subject | Low-rank | - |
dc.subject | Lattice | - |
dc.subject | Convex programming | - |
dc.subject | Sparsity | - |
dc.subject | Patterned fabric inspection | - |
dc.subject | Motif | - |
dc.title | Lattice-based patterned fabric inspection by using total variation with sparsity and low-rank representations | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1137/17M1113138 | - |
dc.identifier.scopus | eid_2-s2.0-85039743959 | - |
dc.identifier.volume | 10 | - |
dc.identifier.issue | 4 | - |
dc.identifier.spage | 2140 | - |
dc.identifier.epage | 2164 | - |
dc.identifier.eissn | 1936-4954 | - |
dc.identifier.isi | WOS:000418654000016 | - |
dc.identifier.issnl | 1936-4954 | - |