Human image understanding: recent research and a theory
Papers from the second workshop Vol. 13 on Human and Machine Vision II
An active vision architecture based on iconic representations
Artificial Intelligence - Special volume on computer vision
Photobook: content-based manipulation of image databases
International Journal of Computer Vision
Relational Matching
Integrating Iconic and Structured Matching
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Probabilistic Object Recognition Using Multidimensional Receptive Field Histograms
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
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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.