Active Pictorial Structures
Active Pictorial Structures
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DOI：
10.1109/cvpr.2015.7299182
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发表时间：
2015-06
期刊：
影响因子：
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通讯作者：
Epameinondas Antonakos;Joan Alabort-i-Medina;S. Zafeiriou
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文献类型：
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作者：
Epameinondas Antonakos;Joan Alabort-i-Medina;S. Zafeiriou
在本文中，我们提出了一种新的生成变形模型的图形结构（PS）和主动外观模型（AAMs）的目标对齐在野外的动机。受PS中使用的树结构的启发，所提出的Active Pictorial Structures（APS）1使用从相邻地标周围区域提取的补丁之间的多个基于图形的成对正态分布（高斯马尔可夫随机场）来建模对象的外观。我们表明，这种配方是更准确的比使用一个单一的多变量分布（主成分分析）通常在文献中做。APS采用具有固定雅可比矩阵和黑森矩阵的加权逆组合高斯-牛顿优化，可实现接近实时的性能和最先进的结果。最后，APS具有类似弹簧的基于图的变形先验项，这使得它们对不良初始化具有鲁棒性。我们提出了广泛的实验上的任务，面部对齐，表明APS优于当前国家的最先进的方法。据我们所知，该方法是第一个加权逆合成技术，证明是如此准确和有效的同时。
In this paper we present a novel generative deformable model motivated by Pictorial Structures (PS) and Active Appearance Models (AAMs) for object alignment in-the-wild. Inspired by the tree structure used in PS, the proposed Active Pictorial Structures (APS)1 model the appearance of the object using multiple graph-based pairwise normal distributions (Gaussian Markov Random Field) between the patches extracted from the regions around adjacent landmarks. We show that this formulation is more accurate than using a single multivariate distribution (Principal Component Analysis) as commonly done in the literature. APS employ a weighted inverse compositional Gauss-Newton optimization with fixed Jacobian and Hessian that achieves close to real-time performance and state-of-the-art results. Finally, APS have a spring-like graph-based deformation prior term that makes them robust to bad initializations. We present extensive experiments on the task of face alignment, showing that APS outperform current state-of-the-art methods. To the best of our knowledge, the proposed method is the first weighted inverse compositional technique that proves to be so accurate and efficient at the same time.