The segmentation effects ended up then proposed to Find a possible boundary region for every object, which was combined with the original images for the fine segmentation with the objects. We validated the designed approach on two general public datasets (

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. The PBR is often a binary picture and marks the region where item boundaries are most likely to look, even though the BEI simply retains the first picture information situated in the PBR and may decrease the influence of redundant background in image segmentation, as revealed in Figure 2.

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We combine these uncertainty maps and initial input images and feed them into your specified network for a far more wonderful segmentation. Soon after executing these three techniques, the network can obtain a significantly improved segmentation general performance.