Iconic indexing by 2-D strings
IEEE Transactions on Pattern Analysis and Machine Intelligence
A theory for qualitative spatial reasoning based on order relations
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Topological relations in the world of minimum bounding rectangles: a study with R-trees
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Qualitative representation of positional information
Artificial Intelligence
Maintaining knowledge about temporal intervals
Communications of the ACM
Qualitative Representation of Spatial Knowledge
Qualitative Representation of Spatial Knowledge
Algorithms for Hierarchical Spatial Reasoning
Geoinformatica
Object-Based Directional Query Processing in Spatial Databases
IEEE Transactions on Knowledge and Data Engineering
Reasoning About Spatial Relationships in Picture Retrieval Systems
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Qualitative and Topological Relationships in Spatial Databases
SSD '93 Proceedings of the Third International Symposium on Advances in Spatial Databases
Composing Cardinal Direction Relations
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Using Orientation Information for Qualitative Spatial Reasoning
Proceedings of the International Conference GIS - From Space to Territory: Theories and Methods of Spatio-Temporal Reasoning on Theories and Methods of Spatio-Temporal Reasoning in Geographic Space
Integrated spatial reasoning in geographic information systems: combining topology and direction
Integrated spatial reasoning in geographic information systems: combining topology and direction
Similarity assessment for cardinal directions between extended spatial objects
Similarity assessment for cardinal directions between extended spatial objects
Fuzzy semantics for direction relations between composite regions
Information Sciences—Informatics and Computer Science: An International Journal
Cardinal directions between spatial objects: the pairwise-consistency problem
Information Sciences—Informatics and Computer Science: An International Journal
Object localization based on directional information case of 2D vector data
GIS '06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
Spatial Relations Analysis by Using Fuzzy Operators
ICCS 2009 Proceedings of the 9th International Conference on Computational Science
A splitting line model for directional relations
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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Directional relation, as a kind of spatial constraints, has been recognized as being an important means for spatial query, analysis and reasoning. Directional relation is conventionally concerned with two point objects. However, in spatial query and analysis, there is also a need of directional relations between point and line, point and area, line and line, line and area, and area and area. Therefore, conventional definition of direction needs to be extended to include line and area objects (i.e. the so-called extended objects). Existing models for directional relation of extended objects make use of approximate representations (e.g. minimum bounding rectangles) of the extended objects so as to produce some results with unrealistic impression. In this paper, a statistical model is presented. In this new model, (1) an extended spatial object is decomposed into small components; (2) the directional relation between extended spatial objects is then determined by the directions between these small components which form a distribution; and (3) two measures (i.e. range and median direction) are utilized to describe the statistical property of the distribution. This statistical model is based upon the (extended) spatial objects themselves, instead of their approximate representations. An experimental test has been carried out and the result indicates that the directional relations computed from this model is very close to those perceived by human beings.