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- W2892474421 abstract "Image segmentation evaluation is popularly categorized into two different approaches based on whether the evaluation uses a human expert’s manual segmentation as a reference or not. When comparing automated segmentation against manual segmentation, also referred to as the ground-truth segmentation, multiple ground-truths are usually available. Much research has been done on analysis of segmentation algorithms and performance metrics, but very little study has been done on analyzing techniques for ground-truth fusion from multiple ground-truth segmentations. We propose a hybrid ground-truth fusion technique for image segmentation evaluation and compare it with other existing ground-truth fusion methods on a data set having multiple ground-truths at various coarseness levels. Qualitative and quantitative results show that the proposed method provides improved segmentation evaluation performance." @default.
- W2892474421 created "2018-10-05" @default.
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- W2892474421 date "2018-04-01" @default.
- W2892474421 modified "2023-09-26" @default.
- W2892474421 title "A Ground-Truth Fusion Method for Image Segmentation Evaluation" @default.
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- W2892474421 doi "https://doi.org/10.1109/ssiai.2018.8470317" @default.
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