Object recognition and localization is still a very challenging problem, despite recent advances in deep learning (DL) approaches, especially for objects with varying shapes and appearances. Statistical models, such as an Active Shape Model (ASM), rely on a parametric model of the object, allowing an easy incorporation of prior knowledge about shape and appearance in a principled way. To take advantage of these benefits, this paper proposes a new ASM framework that addresses two tasks: (i) comparing the performance of several image features used to extract observations from an input image; and (ii) improving the performance of the model fitting by relying on a probabilistic framework that allows the use of multiple observations and is robust to the presence of outliers. The goal in (i) is to maximize the quality of the observations by exploring a wide set of handcrafted features (HOG, SIFT, and texture templates) and more recent DL-based features. Regarding (ii), we use the Generalized Expectation-Maximization algorithm to deal with outliers and to extend the fitting process to multiple observations. The proposed framework is evaluated in the context of facial landmark fitting and the segmentation of the endocardium of the left ventricle in cardiac magnetic resonance volumes. We experimentally observe that the proposed approach is robust not only to outliers, but also to adverse initialization conditions and to large search regions (from where the observations are extracted from the image). Furthermore, the results of the proposed combination of the ASM with DL-based features are competitive with more recent DL approaches (e.g. FCN [1], U-Net [2] and CNN Cascade [3]), showing that it is possible to combine the benefits of statistical models and DL into a new deep ASM probabilistic framework.

中文翻译：

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适用于稳固对象拟合的深度活动形状模型。

尽管最近在深度学习（DL）方法方面取得了进步，但对于形状和外观不同的对象而言，对象识别和定位仍然是一个非常具有挑战性的问题。统计模型（例如活动形状模型（ASM））依赖于对象的参数模型，从而可以轻松地以原则方式合并有关形状和外观的现有知识。为了利用这些好处，本文提出了一个新的ASM框架，该框架解决了两个任务：（i）比较用于从输入图像中提取观察结果的几种图像特征的性能；（ii）依靠概率框架提高模型拟合的性能，该概率框架允许使用多个观察值并且对于异常值的存在具有鲁棒性。（i）中的目标是通过探索各种手工制作的功能（HOG，SIFT和纹理模板）以及最新的基于DL的功能来最大程度地提高观测质量。关于（ii），我们使用广义期望最大化算法来处理离群值并将拟合过程扩展到多个观测值。拟议的框架是在面部标志性拟合和心脏磁共振容积中左心室心内膜分割的背景下进行评估的。我们通过实验观察到，所提出的方法不仅对异常值具有鲁棒性，而且还对不利的初始化条件和较大的搜索区域（从中从图像中提取观测值）具有鲁棒性。此外，