Reconfigurable Frame-Grabber for Real-Time Automated Visual Inspection (RT-AVI) Systems

  • Sergio A. Cuenca
  • Francisco Ibarra
  • Rafael Alvarez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2147)


In most of the automated systems for visual inspection tasks, real time requirements constitute an important aspect to have in to account in the design of them. Often, a frame-grabber attached to a computer using MMX-optimised software libraries is not enough to satisfy the above requirements and it is necessary to use expensive specialised hardware and architectures. Reconfigurable hardware gives us the best of both worlds: the flexibility of software and the high performance of customised hardware. In this paper we present a reconfigurable frame-grabber concept to integrate complex real-time processing functions needed for high-speed line inspection applications directly onboard. This allows the efficient hardware-software co-design to achieve high-performance low-cost solutions.


Frame Grabber Pattern Recognition Letter Grey Level Histogram Main Processor Pixel Feature 
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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  1. 1.
    Matrox, inc.
  2. 2.
    Imaging Technology, inc.
  3. 3.
    Baykut A. et al. Real-Time Defect Inspection of Textured Surfaces. Real-Time Imaging 6, 17–27, 2000.zbMATHCrossRefGoogle Scholar
  4. 4.
    Wei-Bin Chen and Gtan Libert. Real-Time Automatic Visual Inspection of Highspeed Plane Products by Means of Parallelism. Real-Time Imaging 4, 379–388, 1998.CrossRefGoogle Scholar
  5. 5.
  6. 6.
  7. 7.
    S.Hossain Hajimowlana, et. al. An In-Camera Data Stream Processing System for Defect Detection in Web Inspection Tasks. Real-Time Imaging 5, 23–34, 1999.CrossRefGoogle Scholar
  8. 8.
  9. 9.
  10. 10.
    R.M. Haralick. Computer and Robot Vision. Vol I. Addison-Wesley, New York, 1992.Google Scholar
  11. 11.
    P.C. Chen and T. Pavlidis, Segmentation by texture using a co-occurrence matrix and a split-and-merge algorithm, Computer Graphics and Image Processing 10, 172–182, 1979.CrossRefGoogle Scholar
  12. 12.
    D. Harwood, T. Ojala, M. Pietikinen, S. Kelman and L.S. Davis, Texture classification by center-symmetric auto-correlation, using Kullback discrimination of distributions, Pattern Recognition Letters 16, 1–10, 1995.CrossRefGoogle Scholar
  13. 13.
    K. Shiranita, T. Miyajima and R. Takiyama. Determination of meat quality by texture analisis. Pattern Recognition Letters, 19: 1319–1324, 1998.zbMATHCrossRefGoogle Scholar
  14. 14.
    T. Ojala, M. Pietikinen and D. Harwood. A comparative study of texture measures with classification based on feature distributions. Pattern Recognition 29(1): 51–59, 1996.CrossRefGoogle Scholar
  15. 15.

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Sergio A. Cuenca
    • 1
  • Francisco Ibarra
    • 1
  • Rafael Alvarez
    • 1
  1. 1.Departamento de Tecnologίa Informática y ComputaciónUniversidad de AlicanteAlicanteSpain

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