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The normalized cut and image segmentation.
Firstly, an over segmenting of the network is provided by a sophisticated algorithm (Normalized Cut).
On the left the blue points represent the bias (prior) and the biased normalized cut is on the right.
Fiber traces are compared pairwise to create a weighted undirected graph which is partitioned into coherent sets using the normalized cut (N cut) criterion.
We also discuss further extensions that highlight the benefits of our analysis: i) a spectral relax-ation involving thresholded eigenvectors, and ii) a normalized cut graph clustering algorithm that does not fix the number of clusters in the graph.
normalized cut.
These measures include normalized cut, ratio cut, conductance, etc.
(3) Minimize the global normalized cut using a greedy strategy.
The proposed splitting scores are based on normalized cut.
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