Efficient cut-based image segmentation techniques
Dorit Hochbaum
University of California, Berkeley
Wednesday, March 12, 2008
4:00, 1221 CS
Segmenting an image is to determine a partition to the salient features of
the image and identify them as associated with different types of
objects. This is of particular importance in medical imaging where
blur conceals information of critical importance. The MRF presentation
of the problem is formulated as minimization of deviation penalty, from
the captured colors of the pixels, and separation penalty, which is
associated with two adjacent pixels having different colors.
We describe a very efficient and best possible polynomial time algorithm
for a convex variant of the problem. This algorithm's efficiency enables
its use in an interactive set-up. It is more efficient than most procedures
based on spectral techniques, partitioning approaches or heuristic
clustering. We then demonstrate how to apply the procedure for the
purpose of de-blurring medical images and identifying structures hidden
by noise.
Time permitting, we will present additional efficient poly time algorithms for
several types of ratio cuts that have been believed to be "hard".
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