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Cast system approach for visual inspection

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Computer Aided Systems Theory — EUROCAST '95 (EUROCAST 1995)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1030))

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Abstract

Begining with the concepts and techniques of Artificial Vision and Systems Theory, the main goal of this paper is the analysis and synthesis of a formal general model to be the base for the design of visual automatic inspection systems and its implementation and testing in a real case of fault detection using digital images acquired through a camera-computer chain.

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References

  1. Pichler, F. Computer Aided System Theory: A Framework for Interactive Method Banks. Cybernetics and Systems. 88. pp 731–736.The Netherlands 1988.

    Google Scholar 

  2. Candela, S., Muñoz, J., Alayon, F., Garcia C. Un Sistema de Visión para la Detección de Defectos.Actas de Panel'92 p 240–247. Las Palmas de G. Canaria. 1992. Universidad de Las Palmas de Gran Canaria

    Google Scholar 

  3. Unser, M., Coulon, F. Detection of Defects by Texture Monitoring in Automatic Visual Inspection. Proceedings of The 2md International Conference on Robot Vision and Sensory Controls November 1982. Stuttgart, Germany.

    Google Scholar 

  4. Muñoz Blanco, J. A. “Jerarquización de estructuras de nivel bajo y medio para reconocimiento visual. Aplicaciones a texturas y formas. Tesis Doctoral. 1987.

    Google Scholar 

  5. Wang, L.; and D. He. “Texture Classification Using Texture Spectrum”. Pattern Recognition”. 1990.

    Google Scholar 

  6. Amelia C. Fong, Lionel M. Ni, Kwan Y. Wong “Fast Discrimination Between Homogeneous and Texture Regions”.

    Google Scholar 

  7. García, C. Estructuras de Representación Visual Múltiple. Aplicación a la Detección de Variaciones Locales en Texturas y a la Inspección Visual. Tesis Doctoral. 1995.

    Google Scholar 

  8. Muñoz, J., Garcia, C., Alayon, F., Candela, S. Systems Concepts for Visual Texture Change Detection Strategy. Lecture Notes in Computer Science 763. pp 358–366. 1994.

    Google Scholar 

  9. Candela, S., Garcia, C., Muñoz, J., Alayon, F. Facilitatory Process for Contrast Detection. Lecture Notes in Computer Science 720. pp 627–630. 1993.

    Google Scholar 

  10. Borghesi, V. Cantoni, M. Diani.“An Industrial Application of Texture Analysis”.

    Google Scholar 

  11. Conners, R. Identifying and Locating Surface Defects in Wood: Part of an Automated Lumber Processing System. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.Pami-5, No 6, November 1983.

    Google Scholar 

  12. Ade, F. “Application of principal component analisys to the inspection of industrial goods”. Applications of Digital Image Processing, vol 397, pag 216–223, SPIE 1983.

    Google Scholar 

  13. Siew, H., Hodgson, R. “Texture Measures for Carpet Wear Assesment”. Transactions on Pattern Analisys and Machine Intelligence. vol 10,n1.IEEE Computer Society Press. 1988.

    Google Scholar 

  14. Neubauer, C. “Segmentation of Defects in Textile Fabric”. International Conference on Pattern Recognition Proceeding, pp 688–691. IEEE Computer Society Press. 1992.

    Google Scholar 

  15. Haralick, R. Statistical and Structural Approaches to Texture. Proceedings of the IEEE, vol 67, no 5, May 1979.

    Google Scholar 

  16. Calvin C. Gotlieb, Helbert E. Kreyszig.“Texture Descriptors Based on Co-Ocurrences Matrices”. Computer Vision, Graphics, and Image Processing. Academic Press, Inc. 1990.

    Google Scholar 

  17. Rao, K., Ahmed, N. Orthogonal Transforms for Digital Signal Processing. IEEE International Conference on Acustics, Speech and Signal Processing P 136–40, 1976.

    Google Scholar 

  18. D'Astous and M. E. Jernigan. “Texture Discrimination Based On Detailed Measures Of Power Spectrum”. IEEE. 1984.

    Google Scholar 

  19. Eklundh, J. O. “On The Use of Fourier Features for Texture Discrimination”. Computer Graphics and Image Processing. 1979.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Informática y Sistemas, University of Las Palmas de Gran Canaria, Campus Universitario de Tafira, Las Palmas, Canary Islands, 35017 Spain

    S. Candela, C. Garcia, F. Alayon & J. Muñoz

Authors
  1. S. Candela
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  2. C. Garcia
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  3. F. Alayon
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  4. J. Muñoz
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Editor information

Franz Pichler Roberto Moreno Díaz Rudolf Albrecht

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© 1996 Springer-Verlag Berlin Heidelberg

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Candela, S., Garcia, C., Alayon, F., Muñoz, J. (1996). Cast system approach for visual inspection. In: Pichler, F., Díaz, R.M., Albrecht, R. (eds) Computer Aided Systems Theory — EUROCAST '95. EUROCAST 1995. Lecture Notes in Computer Science, vol 1030. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0034781

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  • DOI: https://doi.org/10.1007/BFb0034781

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60748-9

  • Online ISBN: 978-3-540-49358-7

  • eBook Packages: Springer Book Archive

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