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With boundary uncertainty maps and unique input illustrations or photos, we retrain the U-Web for that high-quality segmentation of target objects and will receive an even better efficiency, compared to its coarse segmentation general performance.
Table 8. The results on the created process for the main experiment on fundus and Xray visuals applying diverse values for parameter r in morphological functions and Gaussian filter (brief for rm and rg, respectively).
Immediately after obtaining the boundary uncertainty map and track record excluded impression, we concatenated both of these forms of images and fed them in the segmentation network. Since the concatenated photographs have been different from the original photos and contained little background info, the segmentation network can certainly detect object boundaries and therefore extract The entire object areas accurately using a simple experiment configuration.
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, U-Net) for correct image segmentation. We first train the U-Web to get a coarse segmentation result and afterwards use morphological operations and Gaussian filters to discover a possible boundary location for each target object determined by the acquired final result. The boundary area has a singular depth distribution to point the probability of each and every pixel belonging to object boundaries and is also termed as the boundary uncertainty map (BUM) of the objects.
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