MATLAB Implementation of Image Edge Detection

I have developed a custom image edge detection algorithm that implements multiple edge detection techniques. The algorithm includes implementations of Laplacian, Roberts, Sobel, and Prewitt edge detection methods. These are fundamental image processing algorithms that help identify edge features within images. The Laplacian algorithm detects edges by calculating the second derivative of pixel values, typically implemented using convolution with specific kernels like [0 1 0; 1 -4 1; 0 1 0]. The Roberts algorithm utilizes cross-differences between adjacent pixels, implemented through convolution with 2x2 kernels for diagonal edge detection. Sobel and Prewitt algorithms identify edges by computing gradient magnitude and direction, using horizontal and vertical convolution kernels (such as Sobel's [-1 0 1; -2 0 2; -1 0 1] and [-1 -2 -1; 0 0 0; 1 2 1]) to approximate derivatives in both directions. These algorithms can be applied in various fields including image processing, computer vision, and image recognition systems. The MATLAB implementation typically involves reading images using imread(), converting to grayscale with rgb2gray() if necessary, applying convolution operations using imfilter() or conv2(), and thresholding the results to obtain binary edge maps.