Skip to main content

Cognitive Texture Parameters — the Link to Artificial Intelligence

  • Conference paper
Mustererkennung 1989

Part of the book series: Informatik-Fachberichte ((INFORMATIK,volume 219))

Abstract

The experience with todays texture shows, that it helps only little for the interpretation of CT images in the medical field, since first it is not easy to choose the correct texture parameter from the zoo of exsisting ones and second the texture parameters in general do not match human visual impressions. The following concept shows, how texture parameter can be grouped in families, which gives a better insigth into texture analysis. Each class of texture parameter is represented by a complete set of texture parameters avoiding redundancies. Those families and their texture parameters are adopted to the human texture impressions and are therefore called ‘cognitive texture parameters’.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Literatur

  1. Van Gool, L.; Dewaele, P.; Oosterlinck, A.: Texture Analysis Anno 1983. In: Computer Vision, Graphics and Image Processing 29 (1985) 336–357.

    Article  Google Scholar 

  2. Gernert, D.: Advanced definitions of similarity and their use in classification and related fields. TU München. In Gaul, W.; Schader, M. (eds.): Classification as a tool of research. North-Holland: Elsevier Science Publisher B. V. 1986.

    Google Scholar 

  3. Laws, K.: Textured Image Segmentation. Technical Report, Jan 1980, USCIPI Report 940. Los Angeles, CA 90007: Image Processing Institute, University of Southern California.

    Google Scholar 

  4. Gerthsen, Ch.; Kneser, H.; Vogel, H.: Physik. Berlin: Springer 1977.

    Google Scholar 

  5. Kittel, Charles: Einführung in die Festkörperphysik (Introduction to solid state physics). 6.Aufl. München u.a.: Oldenburg 1983. ISBN 3-486-32766-6.

    Google Scholar 

  6. Serra, J.: Introduction to Mathematical Morphology. In: Computer Vision, Graphics and Image Processing 35 (1986) 114–128.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1989 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Scheppelmann, D., Saurbier, F., Meinzer, H.P., Klemstein, J. (1989). Cognitive Texture Parameters — the Link to Artificial Intelligence. In: Burkhardt, H., Höhne, K.H., Neumann, B. (eds) Mustererkennung 1989. Informatik-Fachberichte, vol 219. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-75102-8_41

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-75102-8_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-51748-1

  • Online ISBN: 978-3-642-75102-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics