Digitial Image Processing Using Local Segmentation
Torsten Seemann
2002

Abstract

A unifying philosophy for carrying out low level image processing called
"local segmentation" is presented. Local segmentation provides a way to
examine and understand existing algorithms, as well as a paradigm for
creating new ones. Local segmentation may be applied to range of important
image processing tasks. Using a traditional segmentation technique in
intensity thresholding and a simple model selection criterion, the  new
FUELS denoising algorithm is shown to be highly competitive with
state-of-the-art algorithms on a range of images. In an effort to improve
the local segmentation, the minimum message length information theoretic
criterion for model selection (MML) is used to select between models
having different structure and complexity. This leads to further
improvements in denoising performance. Both FUELS and the MML variants
thereof require no special user supplied parameters, but instead learn
from the image itself. It is believed that image processing in general
could benefit greatly from the application of the local segmentation
methodology.