Temporal reasoning based on semi-intervals
Artificial Intelligence
Fuzzy Sets and Systems - Special issue on fuzzy methods for computer vision and pattern recognition
Modeling spatial relationships within a fuzzy framework
Journal of the American Society for Information Science - Special issue: management of imprecision and uncertainty
A New Way to Represent the Relative Position between Areal Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maintaining knowledge about temporal intervals
Communications of the ACM
Similarity of Cardinal Directions
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Directional Relations Composition by Orientation Histogram Fusion
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Fuzzy region connection calculus: Representing vague topological information
International Journal of Approximate Reasoning
Fuzzy region connection calculus: An interpretation based on closeness
International Journal of Approximate Reasoning
Speaking with spatial relations
International Journal of Intelligent Systems Technologies and Applications
Relative positions in words: a system that builds descriptions around Allen relations
International Journal of Geographical Information Science
Fuzzy description of topological relations I: a unified fuzzy 9-intersection model
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Fuzzy description of topological relations II: computation methods and examples
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
On internal cardinal direction relations
COSIT'05 Proceedings of the 2005 international conference on Spatial Information Theory
Spatio-temporal reasoning by combined topological and directional relations information
International Journal of Artificial Intelligence and Soft Computing
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Fuzziness is found everywhere, in modeling spatial relations, fuzziness is found at object level as well as in relation semantics. Commonly, fuzzy topological relations are computed between fuzzy objects. Fuzziness in relation semantics is represented by fuzzy topological relations between crisp objects and these types of fuzzy topological relations are much less developed. In this paper, we propose a method for combining fuzzy topological and directional relations. We also propose an algorithm for defuzzification of relations which provides us a binary topological and directional relation between a 2D object pair. These relations are represented in a neighborhood graph. For validation and assessment, a number of experiments have been performed on artificial data.