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Removing Shape-Preserving Transformations in Square-Root Elastic (SRE) Framework for Shape Analysis of Curves
Shantanu H Joshi1, Eric Klassen, Anuj Srivastava
1Dept. of Electrical Engineering, Florida State University, Tallahassee, FL 32310, USA.
Summary
The square-root-elastic (SRE) framework efficiently studies closed curve shapes using elastic metrics and path-straightening. This study extends SRE by removing rotation and re-parameterization to analyze curve shapes more effectively.
Area of Science:
- Computational geometry
- Computer vision
- Differential geometry
Background:
- Studying shapes of closed curves is crucial in various scientific fields.
- Existing frameworks may not adequately handle shape transformations like rotations and re-parameterizations.
- The square-root-elastic (SRE) framework offers a robust approach by combining elastic shape metrics and path-straightening.
Purpose of the Study:
- To extend the square-root-elastic (SRE) framework for analyzing closed curve shapes.
- To incorporate the removal of shape-preserving transformations (rotations and re-parameterizations) into the SRE framework.
- To demonstrate the enhanced framework's effectiveness on 2D and 3D curve data.
Main Methods:
- Development of quotient spaces to handle shape transformations.
- Construction of geodesics on these quotient spaces within the SRE framework.
- Application of the extended SRE framework to experimental datasets of 2D and 3D curves.
Main Results:
- Successfully extended the SRE framework to account for the removal of rotations and re-parameterizations.
- Demonstrated the framework's capability to find meaningful geodesic paths in shape spaces of curves.
- Validated the approach through experimental analysis on diverse 2D and 3D curve datasets.
Conclusions:
- The extended SRE framework provides a more comprehensive method for shape analysis of closed curves.
- Removing shape-preserving transformations enhances the robustness and applicability of geodesic analysis in shape spaces.
- The presented methods offer significant advancements for shape comparison and analysis in computational and geometric applications.
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