Estudio comparativo de técnicas espaciales para la identificación de defectos en textiles
Keywords:
Texture, Image processing, Neural networks, Local binary patterns, Energy of Laws, Co-occurrence matrix
Abstract
This paper uses image processing techniques to detect defects in fabrics. The performance of three spatial techniques is evaluated by statistical descriptors, and the extracted features are classified by neural networks. The texUAN database, developed by GEPRO research group of the Antonio Nariño University, was used in this study.
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References
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[2] Laws K., “Textured Image segmentation,” Ph.D. Dissertation, University of Southern California, January, 1980
[3] Zucker, S.W. , Terzopoulos. Finding structure in coocurrence matrices for texture analysis. Computer Graphics and Image Proc. Vol 2:286-308, 1980
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[5] Clausi D.A., Jernigan M.E., A Fast Method to Determine Co-occurrence Textura Features. IEEE Transaction on Geoscience and Remote Sensing. Vol. 3, pages 610-621, 197
[6] Davies E.R, Machine Vision Theory, Algorithms, Practicalities, El Sevier, 3 edition, 2005
[7] Alvarado J, Fernández J, Análisis de textura en imágenes a escala de grises, utilizando patrones locales binarios (LBP), Memorias XV Simposio De Tratamiento De Señales, Imágenes Y Visión Artificial - STSIVA, Pag 95-100, 2010
[8] Pietikainem M., Image Analysis with Pattern Local Binary. Image Analysis, SCIA 2005 Proceedings, Lecture Notes in Computer Science 3540, Springer, pages 115-118. 2005
[9] Maenpaa T., M. et.al, “Robust Texture Classification by Subsets of Local Binary Patterns”, Proceedings of the 15th International Conference on Pattern Recognition, 2000, Vol. 3, 3-7, Pages 939-942
[10] Harwood D, Ojala T, Pietikäinen M, Kelman S & Davis S, Texture classification by center-symmetric auto-correlation, using Kullback discrimination of distributions. Technical report, Computer Vision Laboratory, Center for Automation Research, University of Maryland, College Park, Maryland. CAR-TR-678, 1993
[2] Laws K., “Textured Image segmentation,” Ph.D. Dissertation, University of Southern California, January, 1980
[3] Zucker, S.W. , Terzopoulos. Finding structure in coocurrence matrices for texture analysis. Computer Graphics and Image Proc. Vol 2:286-308, 1980
[4] Haralick R. M., Textural Features for Image Classification. IEEE Transaction on System, Man, and Cybernetics. Vol. 3, pages 610-621, 1973
[5] Clausi D.A., Jernigan M.E., A Fast Method to Determine Co-occurrence Textura Features. IEEE Transaction on Geoscience and Remote Sensing. Vol. 3, pages 610-621, 197
[6] Davies E.R, Machine Vision Theory, Algorithms, Practicalities, El Sevier, 3 edition, 2005
[7] Alvarado J, Fernández J, Análisis de textura en imágenes a escala de grises, utilizando patrones locales binarios (LBP), Memorias XV Simposio De Tratamiento De Señales, Imágenes Y Visión Artificial - STSIVA, Pag 95-100, 2010
[8] Pietikainem M., Image Analysis with Pattern Local Binary. Image Analysis, SCIA 2005 Proceedings, Lecture Notes in Computer Science 3540, Springer, pages 115-118. 2005
[9] Maenpaa T., M. et.al, “Robust Texture Classification by Subsets of Local Binary Patterns”, Proceedings of the 15th International Conference on Pattern Recognition, 2000, Vol. 3, 3-7, Pages 939-942
[10] Harwood D, Ojala T, Pietikäinen M, Kelman S & Davis S, Texture classification by center-symmetric auto-correlation, using Kullback discrimination of distributions. Technical report, Computer Vision Laboratory, Center for Automation Research, University of Maryland, College Park, Maryland. CAR-TR-678, 1993
Published
2013-11-19
How to Cite
Fernández Gallego, J., & Alvarado Moreno, J. (2013). Estudio comparativo de técnicas espaciales para la identificación de defectos en textiles. ITECKNE, 7(1), 75-82. https://doi.org/https://doi.org/10.15332/iteckne.v7i1.2712
Issue
Section
Research and Innovation Articles
