Fast fourier transforms: a tutorial review and a state of the art
Signal Processing
Periodicity, Directionality, and Randomness: Wold Features for Image Modeling and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining frequency and spatial domain information for fast interactive image noise removal
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Detection and Recognition of Periodic, Nonrigid Motion
International Journal of Computer Vision
View-Invariant Analysis of Cyclic Motion
International Journal of Computer Vision
Robust Real-Time Periodic Motion Detection, Analysis, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovery and Segmentation of Activities in Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Algorithms for Digital Signal Processing
Fast Algorithms for Digital Signal Processing
Non-linear operators in image restoration
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
Multiple motion analysis: in spatial or in spectral domain?
Computer Vision and Image Understanding
Repetitive Motion Analysis: Segmentation and Event Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fusion of Frequency and Spatial Domain Information for Motion Analysis
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Motion Competition: A Variational Approach to Piecewise Parametric Motion Segmentation
International Journal of Computer Vision
Optimal kernels for nonstationary spectral estimation
IEEE Transactions on Signal Processing
Analysis and synthesis of multicomponent signals using positivetime-frequency distributions
IEEE Transactions on Signal Processing
Optimal quadratic detection and estimation using generalized jointsignal representations
IEEE Transactions on Signal Processing
A harmonic retrieval framework for discontinuous motion estimation
IEEE Transactions on Image Processing
Two-dimensional matched filtering for motion estimation
IEEE Transactions on Image Processing
Automatic gait recognition based on statistical shape analysis
IEEE Transactions on Image Processing
State-of-the-art on spatio-temporal information-based video retrieval
Pattern Recognition
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
View-invariant analysis of periodic motion
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Human action recognition using boosted EigenActions
Image and Vision Computing
3D Reconstruction of Periodic Motion from a Single View
International Journal of Computer Vision
IEEE Transactions on Image Processing
Reconstructing and analyzing periodic human motion from stationary monocular views
Computer Vision and Image Understanding
Computational behaviour modelling for autism diagnosis
Proceedings of the 15th ACM on International conference on multimodal interaction
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The analysis of periodic or repetitive motions is useful in many applications, such as the recognition and classification of human and animal activities. Existing methods for the analysis of periodic motions first extract motion trajectories using spatial information and then determine if they are periodic. These approaches are mostly based on feature matching or spatial correlation, which are often infeasible, unreliable, or computationally demanding. In this paper, we present a new approach, based on the time-frequency analysis of the video sequence as a whole. Multiple periodic trajectories are extracted and their periods are estimated simultaneously. The objects that are moving in a periodic manner are extracted using the spatial domain information. Experiments with synthetic and real sequences display the capabilities of this approach.