Edge Detection using Canny Operator with MATLAB Implementation

This text presents edge detection using the Canny operator with MATLAB implementation. The code includes comprehensive explanations of key algorithmic steps: Gaussian filtering for noise reduction using imgaussfilt(), gradient magnitude calculation through Sobel operators with imgradient(), non-maximum suppression for edge thinning, and double thresholding with edge() function for final edge pixel classification. The implementation demonstrates proper parameter selection for sigma values and threshold levels to optimize detection accuracy. Furthermore, we expand the discussion to compare alternative edge detection methodologies, including Sobel operator (using fspecial('sobel') and imfilter()) for simpler gradient-based detection and Laplacian operator (via fspecial('laplacian')) for zero-crossing detection. These techniques find extensive applications in image processing for extracting structural boundary information. The content also covers critical threshold selection strategies using empirical methods or adaptive approaches like Otsu's algorithm (graythresh()) to enhance edge detection performance. Preprocessing techniques are thoroughly examined, including grayscale conversion (rgb2gray()) and Gaussian filtering (imgaussfilt()) with optimal kernel size selection. This comprehensive exploration enables readers to better understand and practically apply edge detection technologies in computer vision applications.