Structural Learning from Iconic Representations
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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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- Structural Learning from Iconic Representations
- Book Title
- Advances in Artificial Intelligence
- Book Subtitle
- International Joint Conference 7th Ibero-American Conference on AI 15th Brazilian Symposium on AI IBERAMIA-SBIA 2000 Atibaia, SP, Brazil, November 19–22, 2000 Proceedings
- pp 399-408
- Print ISBN
- Online ISBN
- Series Title
- Lecture Notes in Computer Science
- Series Volume
- Series ISSN
- Springer Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
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- Editor Affiliations
- 1. Department of Computer Science and Statistics Computational Intelligence Laboratory, University of São Paulo
- 2. Computer Engineering Department Intelligent Techniques Laboratory, University of São Paulo
- Author Affiliations
- 5. Division of Informatics, Edinburgh University, 5 Forrest Hill, EH8 9SH, Edinburgh, Scotland UK
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