Quantifying appearance retention in carpets using geometrical local binary patterns

Authors:
Rolando Quinones;Sergio A. Orjuela;Benhur Ortiz-Jaramillo;Lieva Van Langenhove;Wilfried Philips
Affiliations:
Ghent University, Department of Telecommunications and Information Processing, Ghent University, Department of Textiles, Gent, Belgium and Antonio Narino University, Faculty of Electronic and Biom ...;Ghent University, Department of Telecommunications and Information Processing, Ghent University, Gent, Belgium and Antonio Narino University, Faculty of Electronic and Biomedical Engineering, Bogo ...;Ghent University, Department of Telecommunications and Information Processing, Ghent University, Department of Textiles, Gent, Belgium;Ghent University, Department of Textiles, Gent, Belgium;Ghent University, Department of Telecommunications and Information Processing, Gent, Belgium
Venue:
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Year:
2011

Citing 5
Cited 0

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Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns

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Engineering Applications of Artificial Intelligence
Rotation invariant texture classification using LBP variance (LBPV) with global matching

Pattern Recognition

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Abstract

Quality assessment in carpet manufacturing is performed by humans who evaluate the appearance retention (AR) grade on carpet samples. To quantify the AR grades objectively, different research based on computer vision have been developed. Among them Local Binary Pattern (LBP) and its variations have shown promising results. Nevertheless, the requirements of quality assessment on a wide range of carpets have not been met yet. One of the difficulties is to distinguish between consecutive AR grades in carpets. For this, we adopt an extension of LBP called Geometrical Local Binary Patterns (GLBP) that we recently proposed. The basis of GLBP is to evaluate the grey scale differences between adjacent points defined on a path in a neighbourhood. Symmetries of the paths in the GLBPs are evaluated. The proposed technique is compared with an invariant rotational mirror based LBP technique. The results show that the GLBP technique is better for distinguishing consecutive AR grades in carpets.