Finding geometric and relational structures in an image

  • Radu Horaud
  • Françoise Veillon
  • Thomas Skordas
Features / Shape
Part of the Lecture Notes in Computer Science book series (LNCS, volume 427)


We present a method for extracting geometric and relational structures from raw intensity data. On one hand, low-level image processing extracts isolated features. On the other hand, image interpretation uses sophisticated object descriptions in representation frameworks such as semantic networks. We suggest an intermediate-level description between low- and high-level vision. This description is produced by grouping image features into more and more abstract structures. First, we motivate our choice with respect to what should be represented and we stress the limitations inherent with the use of sensory data. Second, we describe our current implementation and illustrate it with various examples.


Local Symmetry Semantic Network Stereo Match Image Description Subgraph Isomorphism 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1990

Authors and Affiliations

  • Radu Horaud
    • 1
  • Françoise Veillon
    • 1
  • Thomas Skordas
    • 2
  1. 1.LIFIA-IMAG, 46, avenue Félix VialletGrenobleFrance
  2. 2.ITMI, Filiale de CAP-SESA, ZIRST Chemin des PrésMeylanFrance

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