Edge Detection Based on MATLAB for Image Processing

Edge detection is an image processing technique implemented using MATLAB that effectively identifies edge regions within digital images. By leveraging MATLAB's programming environment, developers can implement various edge detection algorithms such as Sobel, Prewitt, Canny, or Laplacian of Gaussian (LoG) methods. These algorithms typically involve gradient calculations, convolution operations with specific kernels, and thresholding techniques to precisely detect edges. The implementation often utilizes MATLAB's Image Processing Toolbox functions including edge(), imgradient(), and fspecial() for kernel generation. Edge detection holds significant applications in computer vision and image processing domains, including object recognition, image segmentation, and edge enhancement. Therefore, mastering edge detection techniques in MATLAB, through both built-in functions and custom algorithm implementation, is crucial for research and practical applications in image processing. The process generally involves loading images using imread(), converting to grayscale with rgb2gray() when necessary, applying edge detection operators, and visualizing results with imshow().