Rhythm
Repetition and Pseudo-Periodicity
Abstract:
The notion of pseudo-periodicity and the related p-norm allow the representation of complex repetitive phenomena as a periodic process plus a set of parameters that define the deviations of that process from true periodicity
Periodicity Transforms
Abstract: Introduces a method of detecting periodicities in data that exploits a series of projections onto "periodic subspaces." The algorithm finds its own set of nonorthogonal basis elements (based on the data), rather than assuming a fixed predetermined basis as in standard transforms.
Discovering High Order Periodic Patterns
Abstract: Discovery of periodic patterns in time series data has become an active research area with many applications. These patterns can be hierarchical in nature, where a higher level pattern may consist of repetitions of lower level patterns. Unfortunately, the presence of noise may prevent these higher level patterns from being recognized in the sense that two portions (of a data sequence) that support the same (high level) pattern may have di erent layouts of occurrences of basic symbols.
Mining Asynchronous Periodic Patterns in Time Series Data
Abstract: Periodicy detection in time series data is a challenging problem of great importance in many applications
Meta-Patterns: Revealing Hidden Periodic Patterns
Abstract: Discovery of periodic patterns in time series data has become an active research area with many applications. These patterns can be hierarchical in nature, where a higher level pattern may consist of repetitions of lower level patterns. Unfortunately, the presence of noise may prevent these higher level patterns from being recognized in the sense that two portions (of a data sequence) that support the same (high level) pattern may have different layouts of occurrences of basic symbols.
Robust Real-Time Periodic Motion Detection, Analysis, and Applications
Abstract: We describe new techniques to detect and analyze periodic motion as seen from both a static and moving camera. By tracking objects of interest, we compute an object's self-similarity as it evolves in time. For periodic motion, the self-similarity measure is also periodic, and we apply Time-Frequency analysis to detect and characterize the periodic motion. The periodicity is also analyzed robustly using the 2-D lattice structures inherent in similarity matrices. A real-time system has been...
Categorical Representation and Recognition of Oscillatory Motion Patterns
Abstract: Many communicative behaviors in the animal kingdom consist of performing and recognizing specialized patterns of oscillatory motion. Here we present an approach to the representation and recognition of these oscillatory motions based on the categorical organization of a simple sinusoidal model having very specific and limited parameter values. This characterization is used to specify the types and layout of computation for recognizing the patterns. Results of the method are demonstrated with
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