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A Unified Framework for Compositional Fitting of Active Appearance Models.

Joan Alabort-I-Medina1, Stefanos Zafeiriou1

  • 1Department of Computing, Imperial College London, 180 Queen's Gate, London, SW7 2AZ UK.

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Summary
This summary is machine-generated.

This study enhances Active Appearance Models (AAMs) fitting using compositional gradient descent (CGD) algorithms. Novel Bayesian cost functions and composition types improve convergence and robustness in computer vision tasks.

Keywords:
Active appearance modelsAsymmetric and bidirectional compositionBayesian inferenceCompositional gradient descentNon-linear optimizationSchur complementWiberg algorithm

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Image Analysis

Background:

  • Active Appearance Models (AAMs) are a foundational technique for modeling deformable objects in computer vision.
  • Fitting AAMs typically involves iterative optimization algorithms.
  • Existing methods for fitting AAMs have limitations in convergence and robustness.

Purpose of the Study:

  • To provide a unified and comprehensive view of compositional gradient descent (CGD) algorithms for fitting AAMs.
  • To introduce novel Bayesian cost functions and composition types to enhance AAM fitting.
  • To offer new insights into existing CGD algorithms through connections to Schur complement and Wiberg methods.

Main Methods:

  • Classification of CGD algorithms based on cost function, composition type, and optimization method.
  • Development of a novel Bayesian cost function as a probabilistic formulation of the project-out loss.
  • Introduction of asymmetric and bidirectional composition types combining image and appearance model gradients.
  • Reinterpretation of existing CGD algorithms using Schur complement and Wiberg methods.

Main Results:

  • A novel Bayesian cost function offers a generalized probabilistic approach to AAM fitting.
  • Asymmetric and bidirectional composition types lead to more convergent and robust CGD algorithms.
  • New perspectives on existing CGD methods reveal connections to advanced mathematical techniques.

Conclusions:

  • The proposed enhancements to CGD algorithms significantly improve the fitting of Active Appearance Models.
  • The study provides a unified framework and novel methods for AAM fitting in computer vision.
  • Publicly available implementation encourages further research and facilitates comparative studies.