Segmentation Quality Metrics
Segmentation Quality Metrics is a formal evaluation framework used in image analysis and computer vision that quantifies the accuracy and consistency of image segmentation algorithms by comparing their output against ground truth labels through specific mathematical measures such as intersection-over-union, Dice coefficient, or boundary fidelity.
416
Publications
41.5K
Citations
1.7K
Authors
685
Institutions
Publications per year
2017–2026
170
Parents
416
A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation
Federico Perazzi, Jordi Pont-Tuset, Brian McWilliams et al. · 2016 · 2K citations
1–5 of 416
1.7K
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École Polytechnique Fédérale de Lausanne, Switzerland
5 publications · 254 citations
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1–5 of 1.7K
685

Centre National de la Recherche Scientifique
Paris, France
12 h-index
54 publications
2.3K citations
University of Pennsylvania
Philadelphia, United States
11 h-index
21 publications
1.4K citations

Imperial College London
London, United Kingdom
9 h-index
34 publications
7.8K citations
Carnegie Mellon University
Pittsburgh, United States
8 h-index
20 publications
1.8K citations
University of Bern
Bern, Switzerland
7 h-index
18 publications
2.3K citations
University of California, Santa Barbara
Santa Barbara, United States
7 h-index
15 publications
931 citations
Heidelberg University
Heidelberg, Germany
7 h-index
25 publications
6.2K citations

University of Toronto
Toronto, Canada
7 h-index
16 publications
2.8K citations
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Repository · 17 publications · 464 citations
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11 publications · 502 citations
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9 publications · 488 citations
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9 publications · 533 citations
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9 publications · 374 citations
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