NDLI: A Unified Framework for Compositional Fitting of Active Appearance Models

Content Provider	Springer Nature Link
Author	Alabort i Medina, Joan Zafeiriou, Stefas
Copyright Year	2016
Abstract	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 novel Bayesian 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 ).
Starting Page	26
Ending Page	64
Page Count	39
File Format	PDF
ISSN	09205691
Journal	International Journal of Computer Vision
Volume Number	121
Issue Number	1
e-ISSN	15731405
Language	English
Publisher	Springer US
Publisher Date	2016-06-09
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Active appearance models Non-linear optimization Compositional gradient descent Bayesian inference Asymmetric and bidirectional composition Schur complement Wiberg algorithm Computer Imaging, Vision, Pattern Recognition and Graphics Artificial Intelligence (incl. Robotics) Image Processing and Computer Vision Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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