Uncertainty in Artificial Intelligence
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Fuzzy Geometric Relations to Represent Hierarchical Spatial Information
Stephane Lapointe, Rene Proulx
A model to represent spatial information is presented in this paper. It is based on fuzzy constraints represented as fuzzy geometric relations that can be hierarchically structured. The concept of spatial template is introduced to capture the idea of interrelated objects in two-dimensional space. The representation model is used to specify imprecise or vague information consisting in relative locations and orientations of template objects. It is shown in this paper how a template represented by this model can be matched against a crisp situation to recognize a particular instance of this template. Furthermore, the proximity measure (fuzzy measure) between the instance and the template is worked out - this measure can be interpreted as a degree of similarity. In this context, template recognition can be viewed as a case of fuzzy pattern recognition. The results of this work have been implemented and applied to a complex military problem from which this work originated.
Keywords: Fuzzy set, fuzzy geometry, spatial information, template, hierarchy, pattern recognit
Pages: 407-415
PS Link: ftp://jupiter.drev.dnd.ca/pub/papers/lapointe/lapointe_uai94.ps.Z
PDF Link: /papers/94/p407-lapointe.pdf
AUTHOR = "Stephane Lapointe and Rene Proulx",
TITLE = "Fuzzy Geometric Relations to Represent Hierarchical Spatial Information",
BOOKTITLE = "Proceedings of the Tenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-94)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1994",
PAGES = "407--415"

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