Congruence, similarity and symmetries of geometric objects
Discrete & Computational Geometry - ACM Symposium on Computational Geometry, Waterloo
Geometric pattern matching under Euclidean motion
Computational Geometry: Theory and Applications - Special issue: computational geometry, theory and applications
Partial matching of planar polylines under similarity transformations
SODA '97 Proceedings of the eighth annual ACM-SIAM symposium on Discrete algorithms
Medical Image Registration Using Geometric Hashing
IEEE Computational Science & Engineering
Similarity Searching for Multi-Attribute Sequences
SSDBM '02 Proceedings of the 14th International Conference on Scientific and Statistical Database Management
Warping indexes with envelope transforms for query by humming
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Indexing multi-dimensional time-series with support for multiple distance measures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Exact indexing of dynamic time warping
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Robust and fast similarity search for moving object trajectories
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Shapes based trajectory queries for moving objects
Proceedings of the 13th annual ACM international workshop on Geographic information systems
3D trajectory matching by pose normalization
Proceedings of the 13th annual ACM international workshop on Geographic information systems
Knowledge and Information Systems
Segmentation and recognition of motion capture data stream by classification
Multimedia Tools and Applications
Constraint-Based Learning of Distance Functions for Object Trajectories
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Fast gesture recognition based on a two-level representation
Pattern Recognition Letters
Trajectory Voting and Classification Based on Spatiotemporal Similarity in Moving Object Databases
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications
Clustering of vehicle trajectories
IEEE Transactions on Intelligent Transportation Systems
Embedding-based subsequence matching in time-series databases
ACM Transactions on Database Systems (TODS)
Finding long and similar parts of trajectories
Computational Geometry: Theory and Applications
Visually exploring movement data via similarity-based analysis
Journal of Intelligent Information Systems
Similarity in (spatial, temporal and) spatio-temporal datasets
Proceedings of the 15th International Conference on Extending Database Technology
Machine learning for vessel trajectories using compression, alignments and domain knowledge
Expert Systems with Applications: An International Journal
Learning to rank biological motion trajectories
Image and Vision Computing
| Hi-index | 0.00 |
For the discovery of similar patterns in 1D time-series, it is very typical to perform a normalization of the data (for example a transformation so that the data follow a zero mean and unit standard deviation). Such transformations can reveal latent patterns and are very commonly used in datamining applications. However, when dealing with multidimensional time-series, which appear naturally in applications such as video-tracking, motion-capture etc, similar motion patterns can also be expressed at different orientations. It is therefore imperative to provide support for additional transformations, such as rotation. In this work, we transform the positional information of moving data, into a space that is translation, scale and rotation invariant. Our distance measure in the new space is able to detect elastic matches and can be efficiently lower bounded, thus being computationally tractable. The proposed methods are easy to implement, fast to compute and can have many applications for real world problems, in areas such as handwriting recognition and posture estimation in motion-capture data. Finally, we empirically demonstrate the accuracy and the efficiency of the technique, using real and synthetic handwriting data.