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Direct curvature scale space: theory and corner detection
1Department of Mathematics, Nanjing University of Aeronautics & astronautics, Nanjing, China. zhbj@nuaa.edu.cn
IEEE Transactions on Pattern Analysis and Machine Intelligence
|January 17, 2007
Summary
This study analyzes the Direct Curvature Scale Space (DCSS) technique for detecting corners in planar curves. A hybrid approach combining CSS and DCSS is proposed to improve noise resilience in corner detection.
Area of Science:
- Computer Vision
- Image Processing
- Computational Geometry
Background:
- Curvature Scale Space (CSS) is a modern image processing tool.
- Direct Curvature Scale Space (DCSS) involves convolving curve curvature with a Gaussian kernel.
Purpose of the Study:
- To theoretically analyze DCSS for planar curve corner detection.
- To investigate the scale-space behavior of corner models.
- To develop a multiscale corner detection method.
Main Methods:
- Theoretical analysis of DCSS on single and double corner models.
- Specification of model properties for DCSS image transformation.
- Development of a hybrid CSS-DCSS strategy.
Main Results:
- DCSS properties enable transformation of DCSS images into tree structures.
- Corners can be detected in a multiscale sense using the proposed method.
- A hybrid strategy mitigates DCSS sensitivity to noise.
Conclusions:
- DCSS is effective for multiscale corner detection in planar curves.
- The proposed hybrid approach enhances robustness against noise.
- This research contributes to advanced corner detection techniques.
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