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Fast motion estimation using bidirectional gradient methods.

Yosi Keller1, Amir Averbuch

  • 1Department of Mathematics, Yale University, New Haven, CT 06520, USA. yosi.keller@yale.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|August 26, 2004
PubMed
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Gradient-based motion estimation (GM) algorithms are enhanced with new bidirectional formulations, improving convergence for large image motions. Analytical convergence analysis and experimental results confirm their applicability to real-world image registration tasks.

Area of Science:

  • Computer Vision
  • Image Processing
  • Medical Imaging

Background:

  • Gradient-based motion estimation (GM) is central to advanced image registration.
  • Existing GM methods handle various motion models but can struggle with large displacements.
  • Image registration requires accurate estimation of pixel and subpixel movements.

Purpose of the Study:

  • To enhance gradient-based motion estimation techniques.
  • To improve the convergence properties of GM for significant image motions.
  • To provide a theoretical analysis of GM convergence.

Main Methods:

  • Introduced two novel bidirectional formulations of gradient-based motion estimation.
  • Performed analytical convergence analysis of the enhanced GM methods.

Related Experiment Videos

  • Validated algorithms using experimental results on real images.

    • Main Results:

    • The new bidirectional GM formulations demonstrate improved convergence for large motions.
    • Analytical analysis provides insights into the properties and convergence of the enhanced methods.
    • Experimental validation confirms the practical applicability of the proposed algorithms.

    Conclusions:

    • Enhanced bidirectional GM methods offer improved performance for image registration.
    • The theoretical analysis supports the practical findings.
    • These advancements are applicable to real-world image analysis scenarios.