MATLAB Implementation and Demonstration of Canny Edge Detector

This repository presents my custom MATLAB implementation and demonstration program for the Canny edge detection operator. Although the program is compact, I aim to share comprehensive implementation details to provide valuable references and assistance for developers. First, let's review the fundamental principles of the Canny operator. As a classical edge detection algorithm, Canny is widely applied in image processing and computer vision domains. Its core methodology involves a sequence of image processing operations to identify edge information within images, thereby facilitating improved target region extraction. Next, I will elaborate on the implementation workflow of the Canny operator with code-specific insights: 1. Image Grayscale Conversion: The implementation begins with converting RGB images to grayscale using MATLAB's rgb2gray() function, which applies luminance-preserving weights to color channels 2. Gaussian Filtering: A 2D Gaussian kernel is convolved with the image to reduce noise interference, where kernel size and sigma parameters can be adjusted for optimal smoothing 3. Gradient Calculation: Sobel operators (horizontal and vertical kernels) are applied to compute gradient magnitude and direction, implemented through convolution operations 4. Non-Maximum Suppression: A pixel-wise comparison algorithm thins edges by preserving local gradient maxima while suppressing non-maximum values along gradient directions 5. Double Thresholding: Hysteresis thresholding using high/low thresholds distinguishes strong, weak, and non-relevant pixels, with connectivity analysis to retain meaningful edge chains 6. Hough Transform Integration: Optional Hough line detection can be incorporated for structural edge analysis, with visualization routines to plot detected edges over original images Through this technical breakdown, readers can develop deeper understanding of Canny's principles and implementation nuances. For those interested in image processing and computer vision, hands-on implementation of the Canny operator will yield substantial learning benefits. Should you encounter any challenges during implementation, feel free to contact me for technical support and guidance.