Optimized Canny Edge Detection Implementation in MATLAB

This custom MATLAB implementation performs edge detection using the optimized Canny operator. The algorithm follows these sequential steps: First, the source image undergoes Gaussian low-pass filtering to reduce noise interference. This is typically implemented using MATLAB's imgaussfilt() function or a custom 2D convolution with a Gaussian kernel, where the standard deviation parameter controls the smoothing intensity. Next, the gradient magnitude and orientation images are computed using Sobel or Prewitt operators. The implementation calculates horizontal and vertical derivatives through convolution, then combines them to determine edge strength and direction using magnitude = sqrt(Gx² + Gy²) and orientation = atan2(Gy, Gx). The third step applies non-maximum suppression to thin edges by preserving only local maxima in the gradient direction. This involves comparing each pixel's magnitude with its neighbors along the gradient direction and suppressing non-maximum values. Finally, double thresholding with connectivity analysis detects edges by: - Marking pixels above the high threshold as strong edges - Identifying pixels between low and high thresholds as weak edges - Converting weak edges to strong edges if they're connected to strong edge pixels through 8-connectivity analysis These steps constitute the complete Canny edge detection methodology implemented in this MATLAB code.