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For each dataset, a tomogram was constructed and segmented as described in "Biomass segmentation" section.
'Application to image segmentation' section discusses image segmentation based on the transformed spectral images to the invariant representation.
The first step is initial segmentation (Section 3.1), where we first apply a modified KMCC algorithm followed by a test for convergence.
Finally, four ellipse estimates are computed from which the operator has to select the best one as the final segmentation (Section 4.3).
False alarms and miss detections were also added to the scenarios based on the amount and the distributions identified by the evaluation of the segmentation (Section 5.1.2).
The classification experiments with oracle segmentation ('Classification experiments with oracle segmentation' section) show a clear improvement compared to the baseline GMM system.
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Then the segmentation sections are given as closed lines of the endo- and epicardium.
We then evaluate the segmentation procedure by comparing the obtained segmentations with manual segmentations (Section 4.4.2).
Section 3 explains the features for segmentation, and Section 4 details the energy minimization scheme.
Section 2.3 describes our method for cone segmentation, and Section 2.4 outlines the method for validation.
Then, we discuss the problem and present an algorithm for secure network topology design and network segmentation in Section 3. Finally, the results and discussions are addressed in Section 4, and the paper is concluded in Section 5.
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