Navigation and mapping in large-scale space
AI Magazine
On the description of the relative position of fuzzy patterns
Pattern Recognition Letters
Weighted fuzzy pattern matching
Fuzzy Sets and Systems - Mathematical Modelling
Approximate spatial reasoning: integrating qualitative and quantitative constraints
International Journal of Approximate Reasoning
Fuzzy Sets and Systems - Special issue on fuzzy methods for computer vision and pattern recognition
A New Way to Represent the Relative Position between Areal Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy Relative Position Between Objects in Image Processing: A Morphological Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Applying soft computing in defining spatial relations
Comparison of spatial relation definitions in computer vision
ISUMA '95 Proceedings of the 3rd International Symposium on Uncertainty Modelling and Analysis
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Toward a geometry of common sense: a semantics and a complete axiomatization of mereotopology
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Computational models for computing fuzzy cardinal directional relations between regions
Knowledge-Based Systems
Modeling and refining directional relations based on fuzzy mathematical morphology
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Modeling cardinal directional relations between fuzzy regions based on alpha-morphology
LoCA'05 Proceedings of the First international conference on Location- and Context-Awareness
| Hi-index | 0.00 |
One of the powerful features of mathematical morphology lies in its strong algebraic structure, that finds equivalents in set theoretical terms, fuzzy sets theory and logics. Moreover this theory is able to deal with global and structural information since several spatial relationships can be expressed in terms of morphological operations. The aim of this paper is to show that the framework of mathematical morphology allows to represent in a unified way spatial relationships in various settings: a purely quantitative one if objects are precisely defined, a semiquantitative one if objects are imprecise and represented as spatial fuzzy sets, and a qualitative one, for reasoning in a logical framework about space.