A Unified Framework for Compositional Fitting of Active Appearance Models

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• 作者：Joan Alabort-i-Medina ; Stefanos Zafeiriou
• 关键词：Active appearance models ; Non ; linear optimization ; Compositional gradient descent ; Bayesian inference ; Asymmetric and bidirectional composition ; Schur complement ; Wiberg algorithm
• 刊名：International Journal of Computer Vision
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：121
• 期：1
• 页码：26-64
• 全文大小：6392KB
• 刊物类别：Computer Science
• 刊物主题：Computer Imaging, Vision, Pattern Recognition and Graphics; Artificial Intelligence (incl. Robotics); Image Processing and Computer Vision; Pattern Recognition;
• 出版者：Springer US
• ISSN：1573-1405
• 卷排序：121

文摘

Active appearance models (AAMs) are one of the most popular and well-established techniques for modeling deformable objects in computer vision. In this paper, we study the problem of fitting AAMs using compositional gradient descent (CGD) algorithms. We present a unified and complete view of these algorithms and classify them with respect to three main characteristics: (i) cost function; (ii) type of composition; and (iii) optimization method. Furthermore, we extend the previous view by: (a) proposing a novelBayesian cost function that can be interpreted as a general probabilistic formulation of the well-known project-out loss; (b) introducing two new types of composition, asymmetric and bidirectional, that combine the gradients of both image and appearance model to derive better convergent and more robust CGD algorithms; and (c) providing new valuable insights into existent CGD algorithms by reinterpreting them as direct applications of the Schur complement and the Wiberg method. Finally, in order to encourage open research and facilitate future comparisons with our work, we make the implementation of the algorithms studied in this paper publicly available as part of the Menpo Project (http://www.menpo.org).