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Fourier Lucas-Kanade algorithm.

Simon Lucey1, Rajitha Navarathna, Ahmed Bilal Ashraf

  • 1Commonwealth Scientific and Industrial Research Organisation (CSIRO), Brisbane QLD 4069, Australia. simon.lucey@csiro.au

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Summary
This summary is machine-generated.

We introduce the Fourier Lucas & Kanade (FLK) algorithm for efficient gradient descent image alignment. FLK enhances robustness to illumination changes and speeds up computation, benefiting object alignment tasks.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Imaging

Background:

  • Classical Lucas & Kanade (LK) algorithm is widely used for image alignment.
  • Traditional LK operates in the spatial domain, limiting its efficiency and robustness to illumination variations.
  • Preprocessing with filter banks can improve performance but adds computational cost.

Purpose of the Study:

  • To propose a novel framework for gradient descent image and object alignment in the Fourier domain.
  • To enhance the efficiency and robustness of the LK algorithm for image matching.
  • To extend the benefits to non-rigid object alignment tasks like Active Appearance Models (AAMs).

Main Methods:

  • Representing source and template images in the complex 2D Fourier domain.
  • Developing the Fourier LK (FLK) algorithm, adapting the classical LK approach.
  • Integrating filter bank preprocessing as a sparse diagonal weighting matrix within FLK.
  • Extending FLK to the Inverse Compositional (IC) form for precomputation.

Main Results:

  • FLK handles substantial illumination variations effectively.
  • Computational cost becomes invariant to the number of filters, improving efficiency.
  • The Inverse Compositional FLK allows for significant precomputation, leading to extreme efficiency and robustness.
  • Demonstrated applicability to non-rigid object alignment tasks.

Conclusions:

  • The Fourier LK (FLK) algorithm offers a computationally efficient and robust alternative for image and object alignment.
  • FLK overcomes limitations of traditional LK, particularly in handling illumination changes and computational load.
  • The method shows promise for advanced applications like Active Appearance Models.