Patch-Based Low-Rank Matrix Completion for Learning of Shape and Motion Models from Few Training Samples
Patch-Based Low-Rank Matrix Completion for Learning of Shape and Motion Models from Few Training Samples
复制标题
基于补丁的低秩矩阵补全，用于从少量训练样本中学习形状和运动模型
DOI：
10.1007/978-3-319-46493-0_43
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
H. Handels
中科院分区：
文献类型：
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作者：
J. Ehrhardt;M. Wilms;H. Handels
统计模型为图像的自动分析开辟了新的可能性。然而，具有代表性的训练数据，如分割图像的可用性有限，导致了统计模型在实践中应用的瓶颈。在本文中，我们提出了一种新的基于面片的技术，它能够使用低阶矩阵补全方法从少量观察到的训练样本中学习具有代表性的形状、外观或运动的统计模型，并提供高质量的细节。我们的方法依赖于这样的假设，即局部变化对遥远地区的影响有限。我们评估了我们的方法在三个典型应用中的应用：(1)人脸的2D形状建模，(2)人类肺部形状的3D建模，以及(3)基于群体的呼吸器官变形的建模。与经典的基于主元分析的建模方法和有限元主元分析方法的比较表明，该模型对小训练集的泛化能力有所提高，表明该模型具有更好的灵活性。
Statistical models have opened up new possibilities for the automated analysis of images. However, the limited availability of representative training data, e.g. segmented images, leads to a bottleneck for the application of statistical models in practice. In this paper, we propose a novel patch-based technique that enables to learn representative statistical models of shape, appearance, or motion with a high grade of detail from a small number of observed training samples using low-rank matrix completion methods. Our method relies on the assumption that local variations have limited effects in distant areas. We evaluate our approach on three exemplary applications: (1) 2D shape modeling of faces, (2) 3D modeling of human lung shapes, and (3) population-based modeling of respiratory organ deformation. A comparison with the classical PCA-based modeling approach and FEM-PCA shows an improved generalization ability for small training sets indicating the improved flexibility of the model.
用于大脑 MR 图像分割的新型 3D 分区活动形状模型
DOI：
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发表时间：
2005
期刊：
International Conference on Medical Image Computing and Computer-Assisted Intervention
影响因子：
--
作者：
Zheen Zhao;S. Aylward;E. Teoh
通讯作者：
E. Teoh
影响因子：
10.9
作者：
Cerrolaza JJ;Reyes M;Summers RM;González-Ballester MÁ;Linguraru MG
通讯作者：
Linguraru MG
DOI：
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发表时间：
2014
期刊：
IEEE Global Conference on Signal and Information Processing
影响因子：
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作者：
Ryan Kennedy;C. J. Taylor;L. Balzano
通讯作者：
L. Balzano