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2D affine-invariant contour matching using B-spline model
1School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. s2633175g@ntu.edu.sg
IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 19, 2007
Summary
This study introduces an affine-invariant B-Spline matching algorithm. It enhances curve matching accuracy and robustness by using Curvature Scale Space (CSS) images, overcoming B-Spline non-uniqueness issues.
Area of Science:
- Computer Vision
- Geometric Modeling
- Image Analysis
Background:
- B-Spline curves offer smooth representations but face non-uniqueness challenges in matching.
- Existing curve matching methods often require point resampling, introducing errors.
- Affine transformations and noise degrade the performance of many matching algorithms.
Purpose of the Study:
- To develop a novel affine-invariant matching algorithm for B-Spline curves.
- To address the non-uniqueness problem inherent in B-Spline curve matching.
- To improve the robustness and accuracy of curve matching against noise and affine transformations.
Main Methods:
- Curve smoothing by increasing B-Spline degree.
- Degree reduction using Least Square Error (LSE) to generate Curvature Scale Space (CSS) images.
- Matching performed in the CSS domain, leveraging its invariance properties.
Main Results:
- The proposed method effectively handles B-Spline non-uniqueness.
- Achieved robustness against noise and affine transformations.
- Demonstrated reduced curve matching error compared to methods requiring resampling.
Conclusions:
- The B-Spline based CSS matching algorithm offers a robust and accurate solution for curve matching.
- The method successfully combines B-Spline continuity with CSS matching advantages.
- Experimental validation confirms the algorithm's effectiveness on similar shape matching tasks.
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