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Wood Texture Analysis by Combining the Connected Elements Histogram and Artificial Neural Networks

  • M. A. Patricio Guisado
  • D. Maravall Gómez-Allende
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2085)

Abstract

The automatic analysis of wood texture, based on a novel concept: the Frequency Histogram of Connected Elements (FHCE) is the main contribution of this work. The FHCE represents the frequency of occurrence of a random event, which not only describes the texture’s gray-level distribution, but also the existing spatial dependence within the texture. The exploitation of the FHCE’s shape, alongside its wavelet transform, allows the computation of excellent features for the discrimination between sound wood and defective wood; in particular, for the really hard pattern recognition problem of detecting cracks in used wood boards. A feedforward multilayer perceptron, trained with the backpropagation algorithm, is the specific ANN classifier applied for the detection and recognition of cracks in wood boards. A large digital image database, developed after an industrial project, has been used for testing purposes, attaining a success ratio far beyond those obtained with more conventional texture analysis and segmentation techniques.

Keywords

Success Ratio Inspection System Crack Detection Discriminant Variable Wood Board 
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 2001

Authors and Affiliations

  • M. A. Patricio Guisado
    • 1
  • D. Maravall Gómez-Allende
    • 2
  1. 1.Serv. Planificación de Sistemas de la InformaciónMadrid
  2. 2.Departamento de Inteligencia ArtificialUniversidad Politécnica de MadridMadrid

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