Defect Detection in Random Colour Textures Using the MIA T2 Defect Maps

  • Fernando López
  • José Manuel Prats
  • Alberto Ferrer
  • José Miguel Valiente
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4142)


In this paper we present a new approach for the detection of defects in random colour textures. This approach is based on the use of the T2 statistic and it is derived from the MIA strategy (Multivariate Image Analysis) developed in recent years in the field of applied statistics. PCA analysis is used to extract a reference eigenspace from a matrix built by unfolding the RGB raw data of defect-free images. The unfolding is performed compiling colour and spatial information of pixels. New testing images are also unfolded and projected onto the reference eigenspace obtaining a score matrix used to compute the T2 images. These images are converted into defect maps which allow the location of defective pixels. Only very few samples are needed to perform unsupervised training. With regard to literature, the method uses one of the simplest approaches providing low computational costs.


Defect Detection Training Image Local Binary Pattern Colour Texture Cumulative Histogram 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Fernando López
    • 1
  • José Manuel Prats
    • 2
  • Alberto Ferrer
    • 2
  • José Miguel Valiente
    • 1
  1. 1.Departament of Computer Science (DISCA) 
  2. 2.Department of Applied Statistics (DEIOAC)Technical University of Valencia (UPV)ValenciaSpain

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