Feb 21, 2018 · Our method takes as input the confidence scores generated by a pixel-dense segmentation network and re-labels pixels with low confidence levels.
Jun 2, 2019 · More specifically, a region growing mechanism aggregates these pixels to neighboring areas with high confidence scores and similar appearance.
This method improves the accuracy of a state-of-the-art fully convolutional semantic segmentation approach on the publicly available COCO and PASCAL ...
Jun 2, 2019 · More specifically, a region growing mechanism aggregates these pixels to neighboring areas with high confidence scores and similar appearance.
Called region growing refinement (RGR), this algorithm uses the score maps available from the CNN to divide the image into regions of high confidence background ...
Jul 10, 2019 · This is a post-processing method → to improve boundary lines → super cool → MC method. (improves segmentation results). Image understanding ...
We introduce a fully unsupervised post-processing algorithm that exploits Monte Carlo sampling and pixel similarities to propagate high-confidence pixel labels ...
May 12, 2020 · We introduce a fully unsupervised post-processing algorithm that exploits Monte Carlo sampling and pixel similarities to propagate high-confidence pixel labels.
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We introduce a fully unsupervised post-processing algorithm that exploits Monte Carlo sampling and pixel similarities to propagate high-confidence pixel labels ...
Semantic segmentation refinement by Monte Carlo region growing of high confidence detections. PA Dias, H Medeiros. Asian Conference on Computer Vision, 131 ...