Advertisement

Automatic Generation of Digital Filters by NN Based Learning: An Application on Paper Pulp Inspection

  • Pascual Campoy-Cervera
  • David F. Mun̄oz-García
  • Daniel Pen̄a
  • José A. Calderón-Martínez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2085)

Abstract

This paper presents an implementation of a digital filtering inspection system applied on a paper pulp sheet production process. The automation of the inspection phase is particularly critical during this process and its solution is highly complex. The system is based on neural network learning, allowing a compromise between resolution and processing speed. The experimental results demonstrating the use of this algorithm for the visual detection of defects in images obtained from a real factory environment are presented. These results show that the developed learning method generates filters that fulfil the required inspection standard.

Keywords

Digital Filter Defect Type Automatic Generation Multi Layer Perceptron Inspection System 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Campoy, P.: Nuevas Tendencias sobre Control Avanzado: Redes Neuronales, pp. 37–53, Fundacion Repsol Publicaciones, 1999.Google Scholar
  2. 2.
    Campoy, P.: Sistemas Inteligentes en la Inspeccin Visual, “Industria XXI” Revista ETSII, Número 0, Universidad Politcnica de Madrid, pp. 23–33, 2000.Google Scholar
  3. 3.
    Conners, R. Identifying and locating surface defects in wood, IEEE Transactions PAMI 5(6) (1983), pp 573–583.Google Scholar
  4. 4.
    Dewaele, P., Van Gool, L., and Oosterlinchk, A. Texture Inspection withself-adaptive convolution filters, Proceedings of the Ninth International Conference on Pattern Recognition, 1, 1988, pp 14–17.Google Scholar
  5. 5.
    Lee, C.S., Choi, C.H., Choi, J.Y., Kim, Y.K. and Choi, S.H., Feature extraction algorithm based on adaptive wavelet packet for surface detect classification, IEEE International Conference on Image Processing, 1996, pp. 673–675.Google Scholar
  6. 6.
    Normas ENCE: Technical Specification of the Product, Empresa Nacional de Celulosas, S.A., 1994.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Pascual Campoy-Cervera
    • 1
  • David F. Mun̄oz-García
    • 1
  • Daniel Pen̄a
    • 1
  • José A. Calderón-Martínez
    • 1
    • 2
  1. 1.Department of Automatic Control, Electrical Engineering and Industrial ComputingUniversidad Politecnica de MadridMadridSpain
  2. 2.Instituto Tecnologico de Aguascalientes, SEP, CONACYTMéxico

Personalised recommendations