Structural Learning from Iconic Representations

  • Herman M. Gomes
  • Robert B. Fisher
Conference paper

DOI: 10.1007/3-540-44399-1_41

Volume 1952 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Gomes H.M., Fisher R.B. (2000) Structural Learning from Iconic Representations. In: Monard M.C., Sichman J.S. (eds) Advances in Artificial Intelligence. Lecture Notes in Computer Science, vol 1952. Springer, Berlin, Heidelberg

Abstract

This paper addresses the important problem of how to learn geometric relationships from sets of iconic (2-D) models obtained from a sequence of images. It assumes a vision system that operates by foveating at interesting regions in a scene, extracting a number of raw primal sketch-like image descriptions, and matching new regions to previously seen ones. A solution to the structure learning problem is presented in terms of a graph-based representation and algorithm. Vertices represent instances of an image neighbourhood found in the scenes. An edge represents a relationship between two neighbourhoods. Intra and inter model relationships are inferred by means of the cliques found in the graph, which leads to rigid geometric models inferred from the image evidence.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Herman M. Gomes
    • 1
  • Robert B. Fisher
    • 1
  1. 1.Division of Informatics, Edinburgh UniversityEdinburghScotland UK