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• W2605201589 abstract "Segmentation algorithms typically require some parameters and their optimal values are not easy to find. Training methods have been proposed to tune the optimal parameter values. In this work we follow an alternative goal of adaptive parameter setting. Considering the popular random walk segmentation algorithm it is demonstrated that the parameter used for the weighting function has a strong influence on the segmentation quality. We propose a hypothesis testing based adaptive approach to automatically setting this parameter, thus adapting the segmentation algorithm to the statistic properties of an image. Our data-driven weighting function is developed under the multiplicative speckle noise model. Since the additive Gaussian noise model is its special case, our method is applicable to a broad range of imaging modalities. Experimental results are presented to demonstrate the usefulness of the proposed approach." @default.
• W2605201589 created "2017-04-14" @default.
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• W2605201589 date "2017-01-01" @default.
• W2605201589 modified "2023-09-27" @default.
• W2605201589 title "T-Test Based Adaptive Random Walk Segmentation Under Multiplicative Speckle Noise Model" @default.
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• W2605201589 doi "https://doi.org/10.1007/978-3-319-54427-4_41" @default.
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