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- W2023471252 abstract "Normalized cut (N-cut) is a rather recent approach to image segmentation representing the image as a graph and using eigenvalues to partition it. However, this method has several parameters that affect the segmentation accuracy. Using pre-set values for these parameters may generate good results for some images and bad results for others. Thus, to achieve maximum segmentation accuracy, these parameters may be manually finetuned for every set of images. This process, of course, would be impractical and lack generality. In this paper, a method is proposed to automatically determine N-cut parameters for every single image based on the image features using evolving fuzzy sets. The proposed method is applied to magnetic reasoning images (MRI) of bladder." @default.
- W2023471252 created "2016-06-24" @default.
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- W2023471252 date "2013-07-01" @default.
- W2023471252 modified "2023-09-26" @default.
- W2023471252 title "N-cuts parameter adjustment using evolving fuzzy inferencing" @default.
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- W2023471252 doi "https://doi.org/10.1109/fuzz-ieee.2013.6622480" @default.
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