A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discrete-time signal processing
Discrete-time signal processing
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
Temporal moving pattern mining for location-based service
Journal of Systems and Software
Motion Trajectory Learning in the DFT-Coefficient Feature Space
ICVS '06 Proceedings of the Fourth IEEE International Conference on Computer Vision Systems
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Automatic fish classification for underwater species behavior understanding
Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams
Detection and classification of highway lanes using vehicle motion trajectories
IEEE Transactions on Intelligent Transportation Systems
Trajectory Clustering and an Application to Airspace Monitoring
IEEE Transactions on Intelligent Transportation Systems
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In this work Discrete Fourier Transform (DFT) and Discrete Wavelet Transform (DWT) were experimentally evaluated for their performances as tools for dimensionality reduction in a real data set of air traffic trajectories. Results showed that both DFT and DWT were able to provide very expressive reduction for trajectory representation with minimal loss of information. Overall, DWT performed slightly better requiring fewer coefficients than DFT to achieve the same signal energy or to provide the same quality of reconstruction of the trajectories.