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结构自适应平滑：图像，功能磁共振成像和弥散加权成像
Structural adaptive smoothing: Images, fMRI and DWI |
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| 课程网址: |
http://videolectures.net/acs07_polzehl_sas/
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| 主讲教师: |
Jörg Polzehl
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| 开课单位: |
魏尔斯特拉斯应用分析与随机研究所
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| 开课时间: |
信息不详。欢迎您在右侧留言补充。
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| 课程语种: |
英语
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| 中文简介: |
本文介绍了 wias 开发的一类结构自适应平滑方法。主要重点将是 polzehl 和 spokoiny (2006年) 中提出的传播分离方法。该方法允许同时确定与规定模型 (结构假设) 有关的同质化区域, 并利用这些信息改进当地估计数。这是通过迭代过程来实现的。名称传播-分离是算法的两个主要属性的同义词。在同质性的情况下, 即如果规定的模型在较大的区域内具有相同的参数, 则该算法实质上提供一系列方差减小的非自适应估计, 并传播到此序列中的最佳估计。分离意味着, 一旦在两个设计点 i 和 j 中检测到估计之间的显著差异, j 中的观测将不会用于估计 j 中的参数。我们建立了一些关于新算法性质的理论 {非渐近} 结果。我们介绍了如何调整这种方法, 以适应不同的成像方式, 从灰度值和彩色图像的去噪, 到分析数据从功能磁共振成像 (fmri) 和扩散加权成像 (dwi) 实验。
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| 课程简介: |
The talk presents a class of structural adaptive smoothing methods developed at WIAS. The main focus will be on the Propagation-Separation (PS) approach proposed in Polzehl and Spokoiny (2006). The method allows to simultaneously identify regions of homogeneity with respect to a prescribed model (structural assumption) and to use this information to improve local estimates. This is achieved by an iterative procedure. The name Propagation-Separation is a synonym for the two main properties of the algorithms. In case of homogeneity, that is if the prescribed model holds with the same parameters within a large region, the algorithm essentially delivers a series of nonadaptive estimates with decreasing variance and propagates to the best estimate from this series. Separation means that, as soon as in two design points i and j significant differences are detected between estimates, observations in j will not be used to estimate the parameter in j. We establish some theoretical {nonasymptotic} results on properties of the new algorithm. We present how this approach can be adjusted to different imaging modalities, ranging from denoising of greyvalue and color images, to the analysis of data from functional Magnetic Resonance Imaging (fMRI) and Diffusion Weigted Imaging (DWI) experiments.
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| 关 键 词: |
功能磁共振成像（fMRI）数据分析; 扩散加权成像（DWI）实验; 传播分离（PS)
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-06-13：zyk
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| 阅读次数: |
36
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