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Segmentation of Spontaneously Splitting Figures into Overlapping Layers

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Pattern Recognition (DAGM 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2191))

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Abstract

We describe a method to decompose binary 2-D shapes into layers of overlapping parts. The approach is based on a perceptual grouping framework known as tensor voting which has been introduced for the computation of region, curve and junction saliencies. Here, we discuss extensions for the creation of modal/amodal completions and for the extraction of overlapping parts augmented with depth assignments. Advantages of this approach are from a conceptual point of view the close relation to psychological findings about shape perception and regarding technical aspects a reduction of computational costs in comparison with other highly iterative methods.

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Author information

Authors and Affiliations

  1. IMA-Lab, University of Hamburg, Germany

    Amin Massad & Bärbel Mertsching

Authors
  1. Amin Massad
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  2. Bärbel Mertsching
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Editor information

Editors and Affiliations

  1. Chair of Image Understanding and Knowledge Based Systems, Technical University Munich, Orleansstr. 34, 81667, Munich, Germany

    Bernd Radig  & Stefan Florczyk  & 

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© 2001 Springer-Verlag Berlin Heidelberg

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Massad, A., Mertsching, B. (2001). Segmentation of Spontaneously Splitting Figures into Overlapping Layers. In: Radig, B., Florczyk, S. (eds) Pattern Recognition. DAGM 2001. Lecture Notes in Computer Science, vol 2191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45404-7_4

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  • DOI: https://doi.org/10.1007/3-540-45404-7_4

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42596-0

  • Online ISBN: 978-3-540-45404-5

  • eBook Packages: Springer Book Archive

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