13,093 research outputs found
Learning the dynamics and time-recursive boundary detection of deformable objects
We propose a principled framework for recursively segmenting deformable objects across a sequence
of frames. We demonstrate the usefulness of this method on left ventricular segmentation across a cardiac
cycle. The approach involves a technique for learning the system dynamics together with methods of
particle-based smoothing as well as non-parametric belief propagation on a loopy graphical model capturing
the temporal periodicity of the heart. The dynamic system state is a low-dimensional representation
of the boundary, and the boundary estimation involves incorporating curve evolution into recursive state
estimation. By formulating the problem as one of state estimation, the segmentation at each particular
time is based not only on the data observed at that instant, but also on predictions based on past and future
boundary estimates. Although the paper focuses on left ventricle segmentation, the method generalizes
to temporally segmenting any deformable object
Machine Analysis of Facial Expressions
No abstract
Graphical model based facial feature point tracking in a vehicle environment
Facial feature point tracking is a research area that can be used in human-computer interaction (HCI), facial expression analysis, fatigue detection, etc. In this paper, a statistical method for facial feature point tracking is proposed. Feature point tracking is a challenging topic in case of uncertain
data because of noise and/or occlusions. With this motivation, a graphical model that incorporates not only temporal information about feature point movements, but also information about the spatial relationships between such points is built. Based on this model, an algorithm that achieves feature point tracking through a video observation sequence is implemented. The proposed method is applied on 2D gray scale real video sequences taken in a vehicle environment and the superiority of this approach over existing techniques is demonstrated
- …