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Automated wear label assessment in carpets by using local binary pattern statistics on depth and intensity images

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Abstract
Carpet customers want a product of which the appearance lasts for years. Therefore, carpet manufacturers certify their products with labels that represent the expected change in appearance after the first year of installation. No automated system exists yet for objectively assigning these ranks. In this approach, we present an automated method for assessing carpet wear based on image analysis. For this, depth and intensity information are captured from eight types of carpet samples. The results show that the method correctly assigns wear labels from 1 to 5 in steps of 1 for six of the eight carpet types.
Keywords
Carpet Wear, Linear Models, Classification, Local Binay Patterns

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Chicago
Orjuela Vargas, Sergio Alejandro, Filip Rooms, Simon De Meulemeester, Robain De Keyser, and Wilfried Philips. 2010. “Automated Wear Label Assessment in Carpets by Using Local Binary Pattern Statistics on Depth and Intensity Images.” In Proceedings of the 2010 IEEE ANDESCON. New York, NY, USA: IEEE.
APA
Orjuela Vargas, S. A., Rooms, F., De Meulemeester, S., De Keyser, R., & Philips, W. (2010). Automated wear label assessment in carpets by using local binary pattern statistics on depth and intensity images. Proceedings of the 2010 IEEE ANDESCON. Presented at the IEEE Andescon & Latincom 2010 : Green technologies for a better world, New York, NY, USA: IEEE.
Vancouver
1.
Orjuela Vargas SA, Rooms F, De Meulemeester S, De Keyser R, Philips W. Automated wear label assessment in carpets by using local binary pattern statistics on depth and intensity images. Proceedings of the 2010 IEEE ANDESCON. New York, NY, USA: IEEE; 2010.
MLA
Orjuela Vargas, Sergio Alejandro, Filip Rooms, Simon De Meulemeester, et al. “Automated Wear Label Assessment in Carpets by Using Local Binary Pattern Statistics on Depth and Intensity Images.” Proceedings of the 2010 IEEE ANDESCON. New York, NY, USA: IEEE, 2010. Print.
@inproceedings{1062689,
  abstract     = {Carpet customers want a product of which the appearance lasts for years. Therefore, carpet manufacturers certify their products with labels that represent the expected change in appearance after the first year of installation. No automated system exists yet for objectively assigning these ranks. In this approach, we present an automated method for assessing carpet wear based on image analysis. For this, depth and intensity information are captured from eight types of carpet samples. The results show that the method correctly assigns wear labels from 1 to 5 in steps of 1 for six of the eight carpet types.},
  articleno    = {224},
  author       = {Orjuela Vargas, Sergio Alejandro and Rooms, Filip and De Meulemeester, Simon and De Keyser, Robain and Philips, Wilfried},
  booktitle    = {Proceedings of the 2010 IEEE ANDESCON},
  isbn         = {9781424467426},
  keyword      = {Carpet Wear,Linear Models,Classification,Local Binay Patterns},
  language     = {eng},
  location     = {Bogot{\'a}, Colombia},
  pages        = {5},
  publisher    = {IEEE},
  title        = {Automated wear label assessment in carpets by using local binary pattern statistics on depth and intensity images},
  url          = {http://dx.doi.org/10.1109/ANDESCON.2010.5632443},
  year         = {2010},
}

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