| 2016 |
Linear Shape Deformation Models With Local Support Using Graph-Based Structured Matrix Factorisation |
Florian Bernard, Peter Gemmar, Frank Hertel, Jorge Goncalves, Johan Thunberg |
Representing 3D shape deformations by high-dimensional linear models has many applications in computer vision and medical imaging. Commonly, using Principal Components Analysis a low-dimensional subspace of the high-dimensional shape space is determined. However, the resulting factors (the most dominant eigenvectors of the covariance matrix) have global support, i.e. changing the coefficient of a single factor deforms the entire shape. Based on matrix factorisation with sparsity and graph-based regularisation terms, we present a method to obtain deformation factors with local support. The benefits include better flexibility and interpretability as well as the possibility of interactively deforming shapes locally. We demonstrate that for brain shapes our method outperforms the state of the art in local support models with respect to generalisation and sparse reconstruction, whereas for body shapes our method gives more realistic deformations. |
用高维线性模型表示3D形状变形在计算机视觉和医学成像中有许多应用。通常，使用主成分分析确定高维形状空间的低维子空间。然而，由此产生的因子（协方差矩阵的最显著特征向量）具有全局支持，即改变单个因子的系数会使整个形状发生变形。基于稀疏和基于图的正则化项的矩阵分解，我们提出了一种获得具有局部支持的变形因子的方法。其好处包括更好的灵活性和可解释性，以及可能交互地局部变形形状。我们演示了对于脑形状，我们的方法在局部支持模型的泛化和稀疏重构方面优于现有技术，而对于身体形状，我们的方法提供了更真实的变形。 |
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| 2016 |
A 3D Morphable Model Learnt From 10,000 Faces |
James Booth, Anastasios Roussos, Stefanos Zafeiriou, Allan Ponniah, David Dunaway |
We present Large Scale Facial Model (LSFM) -- a 3D Morphable Model (3DMM) automatically constructed from 9,663 distinct facial identities. To the best of our knowledge LSFM is the largest-scale Morphable Model ever constructed, containing statistical information from a huge variety of the human population. To build such a large model we introduce a novel fully automated and robust Morphable Model construction pipeline. The dataset that LSFM is trained on includes rich demographic information about each subject, allowing for the construction of not only a global 3DMM but also models tailored for specific age, gender or ethnicity groups. As an application example, we utilise the proposed model to perform age classification from 3D shape alone. Furthermore, we perform a systematic analysis of the constructed 3DMMs that showcases their quality and descriptive power. The presented extensive qualitative and quantitative evaluations reveal that the proposed 3DMM achieves state-of-the-art results, outperforming existing models by a large margin. Finally, for the benefit of the research community, we make publicly available the source code of the proposed automatic 3DMM construction pipeline. In addition, the constructed global 3DMM and a variety of bespoke models tailored by age, gender and ethnicity are available on application to researchers involved in medically oriented research. |
我们提出了大规模人脸模型（LSFM）- 一个由9,663个独特面部身份自动构建的三维可变模型（3DMM）。据我们所知，LSFM是迄今构建的最大规模可变模型，包含来自各种人类群体的统计信息。为了构建这样一个大规模模型，我们引入了一种新颖的全自动和稳健的可变模型构建流程。LSFM所训练的数据集包含每个受试者的丰富人口统计信息，不仅可以构建全局3DMM，还可以为特定年龄、性别或种族群体定制模型。作为一个应用示例，我们利用提出的模型仅通过3D形状进行年龄分类。此外，我们对构建的3DMM进行了系统分析，展示了它们的质量和描述能力。所提出的广泛定性和定量评估表明，该3DMM实现了最新技术成果，远远超过现有模型。最后，为了造福研究界，我们公开了所提出的自动3DMM构建流程的源代码。此外，构建的全局3DMM和各种根据年龄、性别和种族定制的模型可供从事医学研究的研究人员申请使用。 |
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| 2016 |
Active Learning for Delineation of Curvilinear Structures |
Agata Mosinska-Domanska, Raphael Sznitman, Przemyslaw Glowacki, Pascal Fua |
Many recent delineation techniques owe much of their increased effectiveness to path classification algorithms that make it possible to distinguish promising paths from others. The downside of this development is that they require annotated training data, which is tedious to produce. In this paper, we propose an Active Learning approach that considerably speeds up the annotation process. Unlike standard ones, it takes advantage of the specificities of the delineation problem. It operates on a graph and can reduce the training set size by up to 80% without compromising the reconstruction quality. We will show that our approach outperforms conventional ones on various biomedical and natural image datasets, thus showing that it is broadly applicable. |
许多最近的描绘技术在其增强的有效性大部分归功于路径分类算法，这使得可以区分有前景的路径和其他路径。这一发展的缺点是它们需要注释的训练数据，这是繁琐的工作。在本文中，我们提出了一种主动学习方法，可以显著加快注释过程。与标准方法不同，它充分利用了描绘问题的特殊性。它在图上运行，可以将训练集大小减少高达80%，而不会影响重建质量。我们将展示我们的方法在各种生物医学和自然图像数据集上优于传统方法，从而表明它具有广泛的适用性。 |
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| 2016 |
A Nonlinear Regression Technique for Manifold Valued Data With Applications to Medical Image Analysis |
Monami Banerjee, Rudrasis Chakraborty, Edward Ofori, Michael S. Okun, David E. Viallancourt, Baba C. Vemuri |
Regression is an essential tool in Statistical analysis of data with many applications in Computer Vision, Machine Learning, Medical Imaging and various disciplines of Science and Engineering. Linear and nonlinear regression in a vector space setting has been well studied in literature. However, generalizations to manifold-valued data are only recently gaining popularity. With the exception of a few, most existing methods of regression for manifold valued data are limited to geodesic regression which is a generalization of the linear regression in vector-spaces. In this paper, we present a novel nonlinear kernel-based regression method that is applicable to manifold valued data. Our method is applicable to cases when the independent and dependent variables in the regression model are both manifold-valued or one is manifold-valued and the other is vector or scalar valued. Further, unlike most methods, our method does not require any imposed ordering on the manifold-valued data. The performance of our model is tested on a large number of real data sets acquired from Alzhiemers and movement disorder (Parkinsons and Essential Tremor) patients. We present an extensive set of results along with statistical validation and comparisons. |
回归分析是统计数据分析中的一个重要工具，广泛应用于计算机视觉、机器学习、医学影像和各种科学与工程学科中。在向量空间设置中的线性和非线性回归已经得到充分研究。然而，对于流形值数据的推广最近才开始受到关注。除了少数几种方法外，大多数现有的流形值数据回归方法仅限于测地线回归，这是向量空间中线性回归的推广。本文提出了一种新颖的基于核的非线性回归方法，适用于流形值数据。我们的方法适用于回归模型中自变量和因变量都是流形值，或者其中一个是流形值而另一个是向量或标量值的情况。此外，与大多数方法不同，我们的方法不需要对流形值数据施加任何排序。我们的模型在大量从阿尔茨海默病和运动障碍（帕金森病和本体震颤）患者中获取的真实数据集上进行了性能测试。我们提供了一系列广泛的结果，并进行了统计验证和比较。 |
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| 2016 |
Hedgehog Shape Priors for Multi-Object Segmentation |
Hossam Isack, Olga Veksler, Milan Sonka, Yuri Boykov |
Star-convexity prior is popular for interactive single object segmentation due to its simplicity and amenability to binary graph cut optimization. We propose a more general multi-object segmentation approach. Moreover, each object can be constrained by a more descriptive shape prior, "hedgehog". Each hedgehog shape has its surface normals locally constrained by an arbitrary given vector field, e.g. gradient of the user-scribble distance transform. In contrast to star-convexity, the tightness of our normal constraint can be changed giving better control over allowed shapes. For example, looser constraints, i.e. wider cones of allowed normals, give more relaxed hedgehog shapes. On the other hand, the tightest constraint enforces skeleton consistency with the scribbles. In general, hedgehog shapes are more descriptive than a star, which is only a special case corresponding to a radial vector field and weakest tightness. Our approach has significantly more applications than standard single star-convex segmentation, e.g. in medical data we can separate multiple non-star organs with similar appearances and weak edges. Optimization is done by our modified a-expansion moves shown to be submodular for multi-hedgehog shapes. |
星凸性先验在交互式单目标分割中很受欢迎，因为它简单易行，并适用于二进制图割优化。我们提出了一种更通用的多目标分割方法。此外，每个目标可以受到更具描述性的形状先验“刺猬”的约束。每个刺猬形状的表面法线受到任意给定矢量场的局部约束，例如用户标记距离变换的梯度。与星凸性相比，我们的法线约束的紧密程度可以进行更改，从而更好地控制允许的形状。例如，宽松约束，即允许法线的更宽锥形，会产生更放松的刺猬形状。另一方面，最严格的约束会强制骨架与标记一致。总的来说，刺猬形状比星形更具描述性，后者只是对应于径向矢量场和最弱约束的特例。我们的方法比标准的单星凸分割具有更多应用，例如在医学数据中，我们可以分离具有相似外观和边缘弱的多个非星形器官。优化是通过我们修改后的a-扩展移动进行的，已证明对于多刺猬形状是子模的。 |
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| 2016 |
Groupwise Tracking of Crowded Similar-Appearance Targets From Low-Continuity Image Sequences |
Hongkai Yu, Youjie Zhou, Jeff Simmons, Craig P. Przybyla, Yuewei Lin, Xiaochuan Fan, Yang Mi, Song Wang |
Automatic tracking of large-scale crowded targets are of particular importance in many applications, such as crowded people/vehicle tracking in video surveillance, fiber tracking in materials science, and cell tracking in biomedical imaging. This problem becomes very challenging when the targets show similar appearance and the inter-slice/inter-frame continuity is low due to sparse sampling, camera motion and target occlusion. The main challenge comes from the step of association which aims at matching the predictions and the observations of the multiple targets. In this paper we propose a new groupwise method to explore the target group information and employ the within-group correlations for association and tracking. In particular, the within-group association is modeled by a nonrigid 2D Thin-Plate transform and a sequence of group shrinking, group growing and group merging operations are then developed to refine the composition of each group. We apply the propose method to track large-scale fibers from the microscopy material images and compare its performance against several other multi-target tracking methods. We also apply the proposed method to track crowded people from videos with poor inter-frame continuity. |
大规模拥挤目标的自动跟踪在许多应用中尤为重要，例如视频监控中的拥挤人员/车辆跟踪，材料科学中的纤维跟踪以及生物医学成像中的细胞跟踪。当目标具有相似外观且由于稀疏采样、摄像机运动和目标遮挡导致切片/帧间连续性较低时，该问题变得非常具有挑战性。主要挑战来自于关联步骤，旨在匹配多个目标的预测和观测。本文提出了一种新的群组方法，用于探索目标群组信息，并利用群组内的相关性进行关联和跟踪。具体而言，群组内关联由非刚性二维薄板变换建模，然后开发了一系列群组收缩、群组扩展和群组合并操作，以完善每个群组的组成。我们将该方法应用于从显微镜材料图像中跟踪大规模纤维，并将其性能与其他几种多目标跟踪方法进行比较。我们还将该方法应用于跟踪帧间连续性较差的视频中的拥挤人员。 |
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