| Paper: | MA-P7.10 |
| Session: | Motion Detection and Estimation I |
| Time: | Monday, September 17, 09:50 - 12:30 |
| Presentation: |
Poster
|
| Title: |
TIME-VARYING LINEAR AUTOREGRESSIVE MODELS FOR SEGMENTATION |
| Authors: |
Charles Florin; Siemens Corporate Research | | |
| | Nikos Paragios; Ecole Centrale de Paris | | |
| | Gareth Funka-Lea; Siemens Corporate Research | | |
| | James Williams; Siemens Medical Solutions | | |
| Abstract: |
Tracking highly deforming structures in space and time arises in numerous applications in computer vision. Static Models are often referred to as linear combinations of a mean model and modes of variation learned from training examples. In Dynamic Modeling, the shape is represented as a function of shapes at previous time steps. In this paper, we introduce a novel technique that uses the spatial and the temporal information on the object deformation. We reformulate tracking as a high order time series prediction mechanism that adapts itself on-line to the newest results. Samples (toward dimensionality reduction) are represented in an orthogonal basis, and are introduced in an auto-regressive model that is determined through an optimization process in appropriate metric spaces. Toward capturing evolving deformations as well as cases that have not been part of the learning stage, a process that updates on-line both the orthogonal basis decomposition and the parameters of the autoregressive model is proposed. Experimental results with a nonstationary dynamic system prove adaptive AR models give better results than both stationary models and models learned over the whole sequence. |
