Skip to main content
SpringerLink
Log in

Model-based adaptive preprocessing of images in automatic visual inspection

  • Industrial Applications
  • Conference paper
  • First Online:
Computer Analysis of Images and Patterns (CAIP 1993)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 719))

Included in the following conference series:

  • 140 Accesses

Abstract

A new edge detection scheme based on image structural model is described. Developed method for edge extraction allows an explicit quality control during the edge detection and is in the same time not very computationaly expensive. It is used in structure — adaptive algorithms for image binary segmentation in order to solve the problem of defect detection in microelectronics or to perform visual measurements with subpixel accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Chin R. T. and Harlow C.A. Automated visual inspection: A survey // IEEE Trans. Pattern Anal. Machine Intell.,vol. PAMI-4, N∘6, 1982, pp.557–573.

    Google Scholar 

  2. Yoda H. et al. An automatic wafer inspection system using pipelined image processing techniques //IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-10,∘1, 1988, pp.4–16.

    Google Scholar 

  3. Strickland R. N., Draelos T. and Mao Z. Edge detection in machine vision using a simple L1 norm template matching algorithm // Pattern Recognition, vol.23, N∘5, 1990,pp.411–440.

    Google Scholar 

  4. Kundu A. Robust edge detection // Pattern Recognition, vol.23, N∘5, 1989, pp.423–440.

    Google Scholar 

  5. Nagao M., Matsuyama T. Edge preserving smoothing // Computer Graphics and Image Processing.-1979.-vol.9, p.394–407.

    Google Scholar 

  6. Lee S.J. Digital image enhancement and noise filtering by use of local statistics // IEEE Trans. on Pattern Analysis and Machine Intelligence.-1980.-vol. PAMI-2, p. 165–169.

    Google Scholar 

  7. Perez A. and Gonzalez R.C. An iterative thresholding algorithm for image segmentation //IEEE Trans. on Pattern Analysis and Machine Intelligence.-1987.-vol.9,-p.742–751.

    Google Scholar 

  8. Lee S.J. and Chung S. Y. A comparative performance study of several global thresholding techniques for segmentation // Computer Vision, Graphics and Image Processing.-1990.-vol.52,-p.171–190.

    Google Scholar 

  9. Gritsyk V.V. and Palenichka R.M. An adaptive algorithm for image binary segmentation and edge detection // in Proc. 4th Int. Conf. Computer Analysis of Images and Patterns, Sept.,1991 pp. 152–158.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Physics and Mechanics of the Ukrainian, Academy of Sciences, Naukova st.S, 290601, Lviv, Ukraine

    Roman M. Palenichka & Roman T. Mysak

Authors
  1. Roman M. Palenichka
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Roman T. Mysak
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Dmitry Chetverikov Walter G. Kropatsch

Rights and permissions

Reprints and permissions

Copyright information

© 1993 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Palenichka, R.M., Mysak, R.T. (1993). Model-based adaptive preprocessing of images in automatic visual inspection. In: Chetverikov, D., Kropatsch, W.G. (eds) Computer Analysis of Images and Patterns. CAIP 1993. Lecture Notes in Computer Science, vol 719. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57233-3_101

Download citation

  • DOI: https://doi.org/10.1007/3-540-57233-3_101

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-57233-6

  • Online ISBN: 978-3-540-47980-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation