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Affine-invariant geometric shape priors for region-based active contours
Alban Foulonneau1, Pierre Charbonnier, Fabrice Heitz
1Laboratoire Régional des Ponts et Chaussées de Strasbourg, 11 rue Jean Mentelin, BP 9, 67035 Strasbourg, France. alban.foulonneau@equipement.gouv.fr
Summary
This study introduces a novel method for active contour evolution using shape priors based on Legendre moments. This approach enhances geometric flow for improved two-class image segmentation, offering pose and affine invariance.
Area of Science:
- Computer Vision
- Image Processing
- Computational Geometry
Background:
- Active contours are widely used for image segmentation.
- Constraining active contour evolution with shape priors is crucial for accurate segmentation.
- Existing methods may lack robustness to pose and affine deformations.
Purpose of the Study:
- To develop a new method for constraining region-based active contour evolution.
- To incorporate a shape prior based on Legendre moments for improved segmentation.
- To achieve intrinsic invariance to pose and affine deformations in the shape model.
Main Methods:
- Defining a shape prior as the distance between shape descriptors derived from Legendre moments of the characteristic function.
- Minimizing this shape prior to derive a geometric flow.
- Applying the geometric flow in a two-class image segmentation context.
Main Results:
- The proposed method effectively constrains active contour evolution.
- The Legendre moments-based shape prior leads to a beneficial geometric flow.
- The shape model demonstrates intrinsic invariance to pose and affine deformations.
Conclusions:
- The new method offers an effective way to constrain active contour evolution using shape priors.
- Legendre moments provide a robust basis for shape descriptors in segmentation.
- The developed shape model enhances segmentation accuracy and robustness.
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