Shachar Shem-Tov, M.Sc. Thesis Seminar
Wednesday, 28.7.2010, 11:30
We discuss a variational methodology, which involves locally modeling of data
from noisy samples, combined with global model parameter regularization.
We show that this methodology encompasses many previously proposed algorithms,
from the celebrated moving least squares methods to the globally optimal
over-parametrization methods recently published for smoothing and optic flow estimation.
However, the unified look at the range of problems and methods previously
considered also suggests a wealth of novel global functionals and local
Specifically, we show that a new non-local variational functional provided by
this methodology greatly improves robustness and accuracy in local model
recovery compared to previous methods.
The proposed methodology may be viewed as a basis for a general framework for
addressing a variety of common problem domains in signal and image processing
and analysis, such as denoising, adaptive smoothing, reconstruction and segmentation.