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Subpixel edge refinement using deformable models.

Frédéric Bouchara1, Sofiane Ramdani

  • 1UMR CNRS 6168, Laboratoire des Sciences de l'Information et des Systèmes, Université du Sud Toulon-Var, 83957 La Garde Cedex, France. bouchara@univ-tln.fr

Journal of the Optical Society of America. A, Optics, Image Science, and Vision
|April 3, 2009
PubMed
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This summary is machine-generated.

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This study introduces a novel subpixel edge refinement technique using deformable models. The method enhances image edge accuracy by integrating gradient orientation and position information for superior detail.

Area of Science:

  • Computer Vision and Image Processing
  • Computational Geometry

Background:

  • Accurate edge detection is crucial for image analysis.
  • Existing methods often struggle with subpixel precision.

Purpose of the Study:

  • To develop a robust subpixel edge refinement method.
  • To improve the accuracy of edge localization in digital images.

Main Methods:

  • Utilizing deformable models constrained by gradient orientation and position information.
  • Formulating the problem within a statistical maximum a posteriori (MAP) estimation framework.
  • Proposing both a parametric (B-spline) and a sampled deformable model.

Main Results:

  • Demonstrated effectiveness on both synthetic and natural image datasets.

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  • Achieved significant improvements in subpixel edge localization accuracy.
  • Validated the proposed algorithms' adequacy and performance.

    • Conclusions:

    • The proposed deformable model approach effectively refines subpixel edges.
    • Integration of orientation and position information enhances edge detection precision.
    • The statistical framework provides a solid foundation for edge refinement algorithms.