Enhanced Edge Detection in Images Using Improved Phase Congruency Principles
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This paper presents an advanced approach to image edge detection based on refined phase congruency principles. The methodology demonstrates exceptional performance, as evidenced through validation using two sample images and four core algorithmic components: (1) multi-scale oriented filter bank implementation, (2) local energy calculation module, (3) phase deviation analysis subroutine, and (4) adaptive thresholding mechanism. The enhanced technique achieves superior edge localization accuracy and noise robustness compared to conventional methods. Key implementation aspects include Fourier-domain phase analysis using logarithmically spaced Gabor filters and optimized moment-based edge weighting functions. This approach provides deeper insights into computational image processing fundamentals while significantly improving practical applications in computer vision systems.
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