REVIEW 2 cited by
A Survey of Semantic Segmentation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
This survey gives an overview over different techniques used for pixel-level semantic segmentation. Metrics and datasets for the evaluation of segmentation algorithms and traditional approaches for segmentation such as unsupervised methods, Decision Forests and SVMs are described and pointers to the relevant papers are given. Recently published approaches with convolutional neural networks are mentioned and typical problematic situations for segmentation algorithms are examined. A taxonomy of segmentation algorithms is given.
Forward citations
Cited by 2 Pith papers
-
From Pixels to Damage Severity: Estimating Earthquake Impacts Using Semantic Segmentation of Social Media Images
A pixel-level dataset of 547 earthquake images is introduced, and a fine-tuned SegFormer plus a depth-adjusted scoring equation produces damage scores that rise with labeled severity.
-
A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation
The paper proposes a convolution-free transformer pipeline and a thick-to-thin joint loss but reports no experiments and no performance numbers.
Discussion (0). Continue with ORCID to comment.