Image Edge Extraction Using Wavelet Transform Based on Edge Detection Evaluation Criteria
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Resource Overview
This paper applies wavelet transform for image edge extraction following established evaluation criteria. We implement an adaptive threshold-based edge detection method using wavelet transform, validated through computational experiments. Performance comparison with traditional edge detection approaches demonstrates the effectiveness of the proposed methodology through algorithmic implementation and quantitative analysis.
Detailed Documentation
In this paper, we investigate the application of wavelet transform for image edge extraction. Following standard edge detection evaluation metrics, we developed an adaptive threshold-based edgelet edge detection method utilizing wavelet transform, with comprehensive computational validation of the algorithm. The implementation involves multi-scale wavelet decomposition using functions like wavedec2() in MATLAB, followed by adaptive threshold calculation based on subband coefficients. Comparative analysis with conventional edge detection methods (Sobel, Canny) demonstrates the superior performance of our approach through quantitative metrics including precision-recall curves and signal-to-noise ratio measurements. This research contributes significantly to advancing image edge detection technologies, particularly through the integration of multi-resolution analysis and adaptive thresholding mechanisms in computational frameworks.
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