Correlation Degree Calculation for Edge Detection

In this paper, we investigate the applicability of grey correlation degree calculation methods in edge detection and propose a grayscale image edge detection algorithm based on weighted absolute grey correlation degree. The algorithm implementation involves calculating correlation coefficients between pixel neighborhoods using grey relational analysis, where weighted absolute differences determine the degree of association between reference and comparison sequences. We analyze the algorithm's performance through quantitative metrics including precision-recall curves and computational efficiency measurements, comparing it with conventional edge detection methods like Canny and Sobel operators. Our research demonstrates that this algorithm can effectively detect image edges with relatively short computation time, making it highly practical and feasible for real-time applications. Future studies could explore the algorithm's applicability in other domains such as medical imaging or remote sensing, with more extensive experimental validation across diverse datasets. Key implementation considerations include optimizing the weighting function for different image characteristics and handling boundary conditions during neighborhood correlation calculations.