A Point Distribution Model (PDM) is the basis of a Statistical Shape Model
(SSM) that relies on a set of landmark points to represent a shape and
characterize the shape variation. In this work, we present a self-supervised
approach to extract landmark points from a given registration model for the
PDMs. Based on the assumption that the landmarks are the points that have the
most influence on registration, existing works learn a point-based registration
model with a small number of points to estimate the landmark points that
influence the deformation the most. However, such approaches assume that the
deformation can be captured by point-based registration and quality landmarks
can be learned solely with the deformation capturing objective. We argue that
data with complicated deformations can not easily be modeled with point-based
registration when only a limited number of points is used to extract
influential landmark points. Further, landmark consistency is not assured in
existing approaches In contrast, we propose to extract landmarks based on a
given registration model, which is tailored for the target data, so we can
obtain more accurate correspondences. Secondly, to establish the anatomical
consistency of the predicted landmarks, we introduce a landmark discovery loss
to explicitly encourage the model to predict the landmarks that are
anatomically consistent across subjects. We conduct experiments on an
osteoarthritis progression prediction task and show our method outperforms
existing image-based and point-based approaches.

提出了一种基于注册模型的自我监督方法来提取具有统计形状模型（SSM）和点分布模型（PDM）基础的关键点，以更准确地建立解剖上一致的预测关键点，并在骨关节炎进展预测任务中取得比现有基于图像和基于点的方法更好的效果。

通过形变重建和跨对象一致性目标进行自监督的地标学习

Self-supervised Landmark Learning with Deformation Reconstruction and  Cross-subject Consistency Objectives

Facial landmark detection is an important yet challenging task for real-world
computer vision applications. This paper proposes an effective and robust
approach for facial landmark detection by combining data- and model-driven
methods. Firstly, a Fully Convolutional Network (FCN) is trained to compute
response maps of all facial landmark points. Such a data-driven method could
make full use of holistic information in a facial image for global estimation
of facial landmarks. After that, the maximum points in the response maps are
fitted with a pre-trained Point Distribution Model (PDM) to generate the
initial facial shape. This model-driven method is able to correct the
inaccurate locations of outliers by considering the shape prior information.
Finally, a weighted version of Regularized Landmark Mean-Shift (RLMS) is
employed to fine-tune the facial shape iteratively. This
Estimation-Correction-Tuning process perfectly combines the advantages of the
global robustness of data-driven method (FCN), outlier correction capability of
model-driven method (PDM) and non-parametric optimization of RLMS. Results of
extensive experiments demonstrate that our approach achieves state-of-the-art
performances on challenging datasets including 300W, AFLW, AFW and COFW. The
proposed method is able to produce satisfying detection results on face images
with exaggerated expressions, large head poses, and partial occlusions.

本文介绍了一种结合数据和模型的方法来完成面部关键点的检测，即先利用全卷积网络（Fully Convolutional Network, FCN）来生成脸部所有关键点的响应图，再利用 Point Distribution Model（PDM）生成初始面部形状，最后使用加权变体的正则化关键点均值漂移（RLMS）来微调面部形状，实验表明本文提出的方法在面部表情、头部姿态和部分遮挡等挑战性数据集上都能够取得最好的表现。

基于数据驱动和模型驱动的面部关键点鲁棒检测

Combining Data-driven and Model-driven Methods for Robust Facial  Landmark Detection

Rarely in literature a method of segmentation cares for the edit after the
algorithm delivers. They provide no solution when segmentation goes wrong. We
propose to formulate point distribution model in terms of
centripetal-parameterized Catmull-Rom spline. Such fusion brings interactivity
to model-based segmentation, so that edit is better handled. When the delivered
segment is unsatisfactory, user simply shifts points to vary the curve. We ran
the method on three disparate imaging modalities and achieved an average
overlap of 0.879 for automated lung segmentation on chest radiographs. The edit
afterward improved the average overlap to 0.945, with a minimum of 0.925. The
source code and the demo video are available at this http URL

本文提出了一种基于某些方法的分割，能够在分割出现问题时，通过点分布模型来制定 Catmull-Rom 样条，实现交互式的模型分割，在通过改变曲线来处理分割后，我们在三种不同成像模式下进行了测试，平均重叠率为 0.879，通过编辑，平均重叠率提高到 0.945，最低重叠率为 0.925。