Multi-level approach for statistical appearance models with probabilistic correspondences
Multi-level approach for statistical appearance models with probabilistic correspondences
复制标题
具有概率对应的统计外观模型的多级方法
DOI：
10.1117/12.2214885
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Handels H.
中科院分区：
文献类型：
--
作者：
Krüger J;Ehrhardt J;Handels H.
统计形状和外观模型通常基于对训练数据集中一对一对应关系的准确识别。同时，这些相应地标的确定是这些方法中最具挑战性的部分。Hufnagel等人开发了一种使用统计形状模型对应概率的替代方法。在kr<s:1>乌格等人2,3中，我们提出通过将外观信息纳入框架，对统计外观模型使用概率对应。我们采用基于点的图像数据表示，结合位置和外观信息。该模型通过最大后验（MAP）方法进行优化和自适应，该方法根据模型参数和直接影响所考虑结构的形状和外观信息的观测依赖参数推导出单个全局优化准则。由于使用了初始未知的对应概率，并且在模型中引入了更多的自由度，因此对模型生成过程进行正则化是有利的。为此，我们通过正则化项扩展导出的全局准则，该正则化项惩罚不合理的拓扑变化。此外，我们提出了一种多级优化方法，以增加模型生成过程的鲁棒性。
Statistical shape and appearance models are often based on the accurate identification of one-to-one correspondences in a training data set. At the same time, the determination of these corresponding landmarks is the most challenging part of such methods. Hufnagel et al.1developed an alternative method using correspondence probabilities for a statistical shape model. In Krüuger et al.2, 3we propose the use of probabilistic correspondences for statistical appearance models by incorporating appearance information into the framework. We employ a point-based representation of image data combining position and appearance information. The model is optimized and adapted by a maximum a-posteriori (MAP) approach deriving a single global optimization criterion with respect to model parameters and observation dependent parameters that directly affects shape and appearance information of the considered structures. Because initially unknown correspondence probabilities are used and a higher number of degrees of freedom is introduced to the model a regularization of the model generation process is advantageous. For this purpose we extend the derived global criterion by a regularization term which penalizes implausible topological changes. Furthermore, we propose a multi-level approach for the optimization, to increase the robustness of the model generation process.
DOI：
10.1109/iccv.2015.198
发表时间：
2015
期刊：
2015 IEEE International Conference on Computer Vision (ICCV)
影响因子：
--
作者：
Krüger J;Ehrhardt J;Handels H
通讯作者：
Handels H
影响因子：
10.6
作者：
J. Ehrhardt;J. Krüger;H. Handels
通讯作者：
H. Handels