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From high energy physics to low level vision

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Scale-Space Theory in Computer Vision (Scale-Space 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1252))

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Abstract

A geometric framework for image scale space, enhancement, and segmentation is presented. We consider intensity images as surfaces in the (x, I) space. The image is thereby a 2D surface in 3D space for gray level images, and a 2D surface in 5D for color images. The new formulation unifies many classical schemes and algorithms via a simple scaling of the intensity contrast, and results in new and efficient schemes. Extensions to multi dimensional signals become natural and lead to powerful denoising and scale space algorithms. Here, we demonstrate the proposed framework by applying it to denoise and improve gray level and color images.

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Author information

Authors and Affiliations

  1. Lawrence Berkeley National Laboratory, and Dept. of Mathematics, University of California, 94720, Berkeley, CA

    Ron Kimmel & Ravi Malladi

  2. Raymond and Beverly Sackler Faculty of Exact Sciences, Tel-Aviv University, Israel

    Nir Sochen

Authors
  1. Ron Kimmel
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  2. Nir Sochen
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  3. Ravi Malladi
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Editor information

Bart ter Haar Romeny Luc Florack Jan Koenderink Max Viergever

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© 1997 Springer-Verlag Berlin Heidelberg

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Kimmel, R., Sochen, N., Malladi, R. (1997). From high energy physics to low level vision. In: ter Haar Romeny, B., Florack, L., Koenderink, J., Viergever, M. (eds) Scale-Space Theory in Computer Vision. Scale-Space 1997. Lecture Notes in Computer Science, vol 1252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63167-4_54

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  • DOI: https://doi.org/10.1007/3-540-63167-4_54

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63167-5

  • Online ISBN: 978-3-540-69196-9

  • eBook Packages: Springer Book Archive

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