Canny Edge Detection Operator

In the provided implementation, I have developed a complete Canny edge detection algorithm comprising four key stages: Gaussian filtering, non-maximum suppression, double threshold processing, and edge refinement. This multi-step approach effectively identifies edges in digital images while minimizing noise interference. The implementation begins with Gaussian filtering, where we apply a Gaussian convolution kernel to smooth the input image. This preprocessing step reduces high-frequency noise through weighted averaging of pixel intensities within a defined kernel window, typically using a 5x5 or 7x7 kernel with calculated sigma values. Next, non-maximum suppression is performed on the gradient magnitude image (calculated using Sobel or Prewitt operators) to thin broad edges into single-pixel lines. The algorithm compares each pixel's gradient magnitude with its neighbors along the gradient direction, preserving only local maxima while suppressing all other values. Double threshold processing then classifies edge pixels into strong and weak categories using high and low threshold values (commonly with a 3:1 ratio). Pixels with gradient magnitudes above the high threshold are marked as strong edges, while those between the two thresholds become weak edges. This hierarchical classification helps distinguish true edges from noise. Finally, edge refinement connects discontinuous edges through hysteresis tracking. The algorithm recursively examines weak edge pixels adjacent to strong edges, promoting them to strong edges if they form connected paths. This connectivity analysis ensures continuous edge contours while eliminating isolated noise-induced weak edges. Through this systematic implementation combining mathematical operations and logical processing, the algorithm achieves accurate and robust edge detection suitable for various computer vision applications.