## Learning the dynamics and time-recursive boundary detection of deformable objectsSun, Walter and Çetin, Müjdat and Chan, Raymond and Willsky, Alan S. (2008)
Official URL: http://dx.doi.org/10.1109/TIP.2008.2004638 ## AbstractWe 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.
## Available Versions of this Item- Learning the Dynamics and Time-Recursive Segmentation of Deformable Objects. (deposited 30 Oct 2007 10:02)
- Learning the dynamics and time-recursive boundary detection of deformable objects. (deposited 11 Nov 2008 14:01)
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- Learning the dynamics and time-recursive boundary detection of deformable objects. (deposited 11 Nov 2008 14:01)
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