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An Evolvable Hardware Chip for Image Enhancement in Surface Roughness Estimation

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Advances in Natural Computation (ICNC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3612))

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Abstract

Surface roughness is one of the essential quality control processes that the carried out to ensure that manufactured parts conform to specified standards and influences the functional characteristics of the work-piece such as fatigue, fracture resistance and surface friction. The most widely used surface finish parameter in industry is the average surface roughness (Ra) and is conventionally measured by using a stylus type instrument, which has a disadvantage that it requires direct physical contact and may not represent the real characteristics of the surface. Alternately, surface roughness monitoring techniques using non – contact methods based on computer vision technology [1] are becoming popular. In this paper, an evolvable hardware (EHW) configuration using Xilinx Virtex xvc1000 architecture to perform adaptive image processing i.e. noise removal and improve the accuracy of measurement of surface roughness is presented.

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References

  1. Vandenberg, et al.: Digital image processing techniques, fractal dimensionality and scale-space applied to surface roughness. Wear 159, 17–30 (1992)

    Google Scholar 

  2. Layzell, P.: A New research Tool for Intrinsic Hardware Evolution. In: Sipper, M., Mange, D., Pérez-Uribe, A. (eds.) ICES 1998. LNCS, vol. 1478, pp. 47–56. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  3. Poli, R.: Genetic Programming for Image Analysis. Genetic Programming. In: Proceedings of the first Annual Conference, pp. 363–368 (1996)

    Google Scholar 

  4. Harris, C., Buxton, B.: Evolving Edge Detectors. Research Note RN/96/3. University College London (1996)

    Google Scholar 

  5. Ross, B., Feutan, F., Yashkir, D.: Edge Detection of Petrigraphic Images using Genetic Programming

    Google Scholar 

  6. Brock Computer Science Technical Reports, Brock University, Ontario, Canada CS-00-01 (2000)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Research Scholar, Dept of Manufacturing Engg., Anna University, India

    M. Rajaram Narayanan

  2. Professor & Head, Dept of Manufacturing Engg., Anna University, India

    S. Gowri

  3. Member IEEE and Professor, Electronics and Communication, Dr. M.G.R. Engg., College, India

    S. Ravi

Authors
  1. M. Rajaram Narayanan
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  2. S. Gowri
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  3. S. Ravi
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. School of Software, Sun Yat-Sen University, 510275, Guangzhou, China

    Ke Chen

  3. School of Computer Engineering, Nanyang Technological University, BLK N4, 2b-39, Nanyang Avenue, 639798, Singapore

    Yew Soon Ong

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© 2005 Springer-Verlag Berlin Heidelberg

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Narayanan, M.R., Gowri, S., Ravi, S. (2005). An Evolvable Hardware Chip for Image Enhancement in Surface Roughness Estimation. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902_43

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  • DOI: https://doi.org/10.1007/11539902_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28320-1

  • Online ISBN: 978-3-540-31863-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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