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A width-invariant property of curves based on wavelet transform with a novel wavelet function.
Lihua Yang1, C Y Suen, Yuan Yan Tang
1Dept. of Sci. Comput. & Comput. Application, Zhongshan Univ., Guangzhou, China.
Summary
This study introduces a novel wavelet transform method for edge characterization. The maximum moduli of the wavelet transform (MMWT) generates symmetrical curves, enabling a new approach to image curve skeletonization.
Area of Science:
- Image processing
- Computer vision
- Signal analysis
Background:
- Edge characterization is crucial in image analysis.
- Existing methods for curve skeletonization can be complex.
- Wavelet transforms offer powerful tools for signal and image analysis.
Purpose of the Study:
- To improve edge characterization techniques.
- To introduce a novel method for obtaining curve skeletons.
- To leverage the properties of wavelet transforms for image analysis.
Main Methods:
- Development of a novel wavelet function.
- Application of the maximum moduli of the wavelet transform (MMWT) to curves.
- Analysis of the relationship between wavelet scale (s) and curve width (d).
Main Results:
- The MMWT produces two symmetrical curves flanking the original curve.
- The distance between these symmetrical curves is independent of the original curve's width (d) when s/spl ges/d.
- This property facilitates a new method for curve skeleton extraction.
Conclusions:
- The proposed MMWT method offers an effective way to characterize edges.
- This technique provides a robust and novel approach to image curve skeletonization.
- The scale-dependent property of the MMWT is key to its skeletonization capability.
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