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Updated: Aug 9, 2026
11:09
Construction of a Realistic, Whole-Body, Three-Dimensional Equine Skeletal Model using Computed Tomography Data
Published on: February 25, 2021
Correcting curvature-density effects in the Hamilton-Jacobi skeleton
Andrea Torsello1, Edwin R Hancock
1Dipartimento di Informatica, Ca' Foscari University 4 Venice, Venice Mestre 30172, Italy. torsello@dsi.unive.it
Summary
A new method improves 2-D shape skeleton extraction by accounting for boundary curvature variations. This overcomes limitations of the Hamilton-Jacobi approach, offering better endpoint localization and reduced sensitivity to noise and parameters.
Area of Science:
- Computer Vision
- Image Processing
- Computational Geometry
Background:
- The Hamilton-Jacobi approach is a standard method for 2-D shape skeleton extraction.
- This method relies on flux calculations at object boundaries.
- Limitations include pixel resolution, curvature effects, and threshold dependency, especially near endpoints.
Purpose of the Study:
- To develop a novel 2-D shape skeleton extraction algorithm.
- To overcome the endpoint localization challenges and parameter sensitivity of the Hamilton-Jacobi method.
- To improve the robustness and accuracy of skeleton extraction.
Main Methods:
- A new method is proposed that eliminates curvature contribution to error in flux calculation.
- This is achieved by considering density variations due to boundary curvature.
- The approach aims for subpixel precision without traditional interpolation.
Main Results:
- The developed algorithm provides improved localization of shape skeletons.
- It demonstrates reduced susceptibility to boundary noise.
- The method is less dependent on parameter choices compared to the Hamilton-Jacobi method.
Conclusions:
- The novel skeletonization algorithm offers superior performance over the Hamilton-Jacobi method.
- It achieves better endpoint accuracy and robustness.
- This approach presents a significant advancement in 2-D shape analysis.
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