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A graph network for image segmentation

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Advances in Computer Vision

Part of the book series: Advances in Computing Science ((ACS))

Abstract

Image segmentation as one of the oldest problems in image processing and computer vision is, despite of various attempts to solve it [1], not yet solved satisfactorily. Having in mind the huge capability of the human visual system, highly parallel and pipelined computation seems to be necessary for success in this field. According to Uhr [2] parallel-serial layered architectures are best suited for image analysis. In this sense, a new Layered Graph Network (LGN) was developed [3], which is presented and applied to the processing of simulated and real world images.

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Reference

  1. Haralick, R.M., Shapiro, L.G.: Image Segmentation Techniques. CVGIP 29, pp. 100–132 (1985)

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  2. Uhr, L.: Psychological Motivation and Underlying Concepts. In: Tanimoto,S., Klinger, A. (Eds.). Structured Computer Vision. New York: Academic Press 1980

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  3. Jahn, H.: Image Segmentation with a Layered Graph Network. SPIE Proceedings, Vol. 2662, pp. 217–228 (1996)

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  4. Levine, M.D.: Vision in Man and Machine. New York: Mc Graw-Hill 1985

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  5. Pavlidis, T.: Structural Pattern Recognition. Berlin: Springer-Verlag 1977

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  6. Jolion, J.M., Rosenfeld, A.: A Pyramid Framework for Early Vision. Dordrecht: Kluwer Academic Publishers 1994

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  7. Jahn, H.: Eine Methode zur Clusterbildung in metrischen Räumen. Bild & Ton 39, pp. 362–370 (1986)

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Authors
  1. Herbert Jahn
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Editor information

Editors and Affiliations

  1. Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia

    Franc Solina

  2. Abt. Mustererkennung und Bildverarbeitung, TU Wien, Vienna, Austria

    Walter G. Kropatsch

  3. Computer Science Department, Auckland University, Auckland, New Zealand

    Reinhard Klette

  4. Computer and Information Science, GRASP Laboratory, University of Pennsylvania, Philadelphia, USA

    Ruzena Bajcsy

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© 1997 Springer-Verlag/Wien

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Jahn, H. (1997). A graph network for image segmentation. In: Solina, F., Kropatsch, W.G., Klette, R., Bajcsy, R. (eds) Advances in Computer Vision. Advances in Computing Science. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6867-7_4

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  • DOI: https://doi.org/10.1007/978-3-7091-6867-7_4

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-83022-2

  • Online ISBN: 978-3-7091-6867-7

  • eBook Packages: Springer Book Archive

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