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To determine the effectiveness of the segmentation and tracking, we manually counted the number of parasites in five videos (See Table 4).
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To evaluate the effectiveness of these segmentation methods, we use a supervised evaluation method, i.e., misclassification error (ME) [48].
The effectiveness of the proposed segmentation approach on a variety of synthetic and textured images is highlighted.
Lots of empirical results demonstrate the effectiveness and efficiency of the CAD mesh segmentation method in this paper.
This shows the effectiveness of the proposed segmentation-based method to explore discriminatory information from the SPINE-M (similar to the PSSM).
It shows the effectiveness of the proposed segmentation-based method to explore discriminatory information embedded in the PSSM compared to use of whole protein sequence as a general entity.
Moreover, the effectiveness of power segmentation for the grid integration of the proposed system is discussed.
The effectiveness of the proposed PolSAR segmentation approach is verified.
To evaluate the effectiveness of the proposed methods, segmentation accuracy and time complexity have been considered.
To date, the most common method for evaluating the effectiveness of a segmentation method is subjective evaluation, in which a human visually compares the image segmentation results for separate segmentation algorithms, which is a tedious process and inherently limits the depth of evaluation to a relatively small number of segmentation comparisons over a predetermined set of images.
We show visualizations of the large-scale vascular network and demonstrate the effectiveness of vessel segmentation using threshold-based skeletonization algorithms and level-sets.
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