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Updated: Jul 7, 2026
03:31
End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
Published on: December 15, 2023
A mean field annealing approach to robust corner detection
K Sohn1, H Jung, W E Alexander
1Dept. of Radio Sci. & Eng., Yonsei Univ., Seoul.
Summary
This study introduces mean field annealing for improved corner detection. This method effectively preserves corners while removing noise, enhancing corner detection accuracy compared to previous regularization techniques.
Area of Science:
- Computer Vision
- Image Processing
- Computational Mathematics
Background:
- Accurate corner detection is crucial for image analysis and computer vision tasks.
- Previous work utilized constrained regularization for boundary smoothing and curvature estimation.
- Limitations in noise handling and corner preservation were identified in prior methods.
Purpose of the Study:
- To develop an improved boundary smoothing method for curvature estimation.
- To enhance the capability of detecting corners in digital images.
- To introduce a novel approach using mean field annealing for corner detection.
Main Methods:
- Boundary smoothing for curvature estimation using mean field annealing (MFA).
- MFA as a deterministic approximation to simulated annealing for noise reduction.
- Corner sharpness analysis for evaluating detected corners.
Main Results:
- The MFA method effectively removes noise while preserving sharp corners.
- Improved corner detection accuracy and ease of detection compared to constrained regularization.
- Demonstrated effectiveness through matching results based on MFA-detected corners.
Conclusions:
- Mean field annealing offers a superior approach to boundary smoothing for corner detection.
- The proposed method enhances corner detection performance in noisy images.
- The algorithm shows significant potential for various computer vision applications.
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