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We have examined the performance of the segmentation algorithm based on the proposed invariant representation for spectral images.
It exhibits state-of-art boundary adherence more efficiently and improves the performance of the segmentation algorithm.
In this procedure, KHM algorithm instead of K-means algorithm is used to improve stability and performance of the segmentation.
These differences are likely too small to cause any substantial difference in the performance of the segmentation methods.
Judging based on recently published articles, one can conclude that making improvement to the performance of the segmentation and feature extraction modules has received the most attention.
It can be observed that performance of the segmentation system is good for expiratory phases, while it is relatively poor for inspiratory phases.
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Table 2 shows the performances of the segmentation scheme obtained for ComplOverlap and the graphic oracle with three extractors, applying thresholds with α=2.
Note that this is different from evaluating the performance for the segmentation of elephants which is not the focus of our investigation.
Care should be taken by users to validate the performance of these segmentation algorithms on their data.
Experiments were conducted to evaluate the performance of the proposed segmentation, grasping and recognition algorithms, respectively.
To evaluate the performance of the proposed segmentation algorithm, its accuracy was recorded.
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