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T-Test Based Adaptive Random Walk Segmentation Under Multiplicative Speckle Noise Model

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Computer Vision – ACCV 2016 Workshops (ACCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10117))

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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.

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Acknowledgements

Ang Bian was supported by the China Scholarship Council (CSC). Xiaoyi Jiang was supported by the Deutsche Forschungsgemeinschaft (DFG): SFB656 MoBil (project B3) and EXC 1003 Cells in Motion – Cluster of Excellence.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Münster, Münster, Germany

    Ang Bian & Xiaoyi Jiang

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  1. Ang Bian
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  2. Xiaoyi Jiang
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Correspondence to Xiaoyi Jiang .

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Editors and Affiliations

  1. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Chu-Song Chen

  2. Tsinghua University, Beijing, China

    Jiwen Lu

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Kai-Kuang Ma

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Bian, A., Jiang, X. (2017). T-Test Based Adaptive Random Walk Segmentation Under Multiplicative Speckle Noise Model. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10117. Springer, Cham. https://doi.org/10.1007/978-3-319-54427-4_41

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  • DOI: https://doi.org/10.1007/978-3-319-54427-4_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54426-7

  • Online ISBN: 978-3-319-54427-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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