Relation-based aggregation: finding objects in large spatial datasets

Authors:
Xingang Huang;Feng Zhao
Affiliations:
Department of Computer and Information Science, The Ohio State University, 2015 Neil Avenue, Columbus, OH 43210, USA. E-mail: huang@cis.ohio-state.edu/ URL: http://www.cis.ohio-state.edu/~huang;Xerox Palo Alto Research Center, 3333 Coyote Hill Road, Palo Alto, CA 94304, USA. E-mail: zhao@parc.xerox.com/ URL: http://www.parc.xerox.com/zhao
Venue:
Intelligent Data Analysis
Year:
2000

Citing 20
Cited 5

The Curvature Primal Sketch

IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Based Description and Recognition of Planar Curves and Two-Dimensional Shapes

IEEE Transactions on Pattern Analysis and Machine Intelligence
Perceptual Organization and Curve Partitioning

IEEE Transactions on Pattern Analysis and Machine Intelligence
Marching cubes: A high resolution 3D surface construction algorithm

SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Robust Contour Decomposition Using a Constant Curvature Criterion

IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational kinematics

Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Visualizing Features and Tracking Their Evolution

Computer
Extracting and representing qualitative behaviors of complex systems in phase space

Artificial Intelligence
Segmentation of digital plane curves: a dynamic focusing approach

Pattern Recognition Letters
Influence-based model decomposition

AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
On the approximation of curves by line segments using dynamic programming

Communications of the ACM
Further remarks on line segment curve-fitting using dynamic programming

Communications of the ACM
Finding Aggregate Proximity Relationships and Commonalities in Spatial Data Mining

IEEE Transactions on Knowledge and Data Engineering
Locating Perceptually Salient Points on Planar Curves

IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparametric Segmentation of Curves into Various Representations

IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient and Effective Clustering Methods for Spatial Data Mining

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
The spatial aggregation language for modeling and controlling distributed physical systems

The spatial aggregation language for modeling and controlling distributed physical systems
Relsa: automatic analysis of spatial data sets using visual reasoning techniques with an application to weather data analysis

Relsa: automatic analysis of spatial data sets using visual reasoning techniques with an application to weather data analysis
Spatial aggregation: theory and applications

Journal of Artificial Intelligence Research
A qualitative model of physical fields

AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2

Qualitative spatial reasoning: extracting and reasoning with spatial aggregates

AI Magazine
Automated detection of qualitative spatio-temporal features in electrocardiac activation maps

Artificial Intelligence in Medicine
Automatic identification of weather systems from numerical weather prediction data using genetic algorithm

Expert Systems with Applications: An International Journal
Structure Discovery from Massive Spatial Data Sets Using Intelligent Simulation Tools

Computational Discovery of Scientific Knowledge
Electrocardiographic imaging: towards automated interpretation of activation maps

AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

Regularities exist in datasets describing spatially distributed physical phenomena. Human experts often understand and verbalize the regularities as abstract spatial objects evolving coherently and interacting with each other in the domain space. We describe a novel computational approach for identifying and extracting these abstract spatial objects through the construction of a hierarchy of spatial relations. We demonstrate the approach with an application to finding pressure trough features in weather data sets.