Name
Abstract | ||
|---|---|---|
Dynamic mode decomposition (DMD) is a method to extract coherent modes from nonlinear dynamical systems. In this paper, we propose an extension of DMD, sparse nonnegative DMD, which generates a nonlinear and sparse modal representation of dynamics In particular, this makes DMD more suitable for video processing. We reformulate DMD as a block-multiconvex optimization problem to impose constraints and regularizations directly on the structures of the estimated dynamic modes. We introduce the results of experiments with synthetic data and a surveillance video dataset and show that sparse nonnegative DMD can extract part based dynamic modes from video streams. |
Year | Venue | Keywords |
|---|---|---|
2017 | 2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | Dynamic mode decomposition, dynamical systems, sparse modeling, nonnegative decomposition |
Field | DocType | ISSN |
Dynamic mode decomposition,Video processing,Nonlinear system,Pattern recognition,Computer science,Nonlinear dynamical systems,Synthetic data,Artificial intelligence,Optimization problem,Modal,Aerodynamics | Conference | 1522-4880 |
Citations | PageRank | References |
2 | 0.37 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Naoya Takeishi | 1 | 30 | 7.16 |
| Kawahara, Yoshinobu | 2 | 317 | 31.30 |
| Takehisa Yairi | 3 | 294 | 29.82 |