Edge Detection Functions Implemented in MATLAB

This implementation provides MATLAB functions for edge detection, featuring multiple classical operators including Sobel, Canny, Robert, Prewitt, and Marr operators. Edge detection represents a crucial step in digital image processing, effectively extracting boundary and contour information from images. The Sobel operator is a classical edge detection algorithm that calculates edge strength and direction based on grayscale differences between pixels. In MATLAB implementation, this typically involves convolving the image with horizontal and vertical gradient kernels using the conv2 function, then computing the gradient magnitude. The Canny operator offers more precise and stable edge detection, employing Gaussian filtering and non-maximum suppression for optimal edge extraction. The MATLAB implementation generally follows these steps: noise reduction using imgaussfilt, gradient calculation, non-maximum suppression with edge thinning, and double thresholding with hysteresis tracking. Robert and Prewitt operators are also commonly used edge detection algorithms. The Robert operator works with diagonal pixel differences, while Prewitt utilizes gradient-based calculations. Both can be implemented efficiently using kernel convolution operations with predefined filter matrices. The Marr operator, based on the Laplacian of Gaussian (LoG) approach, captures fine edge details in images. The MATLAB implementation typically involves applying Gaussian smoothing followed by Laplacian filtering, often using the fspecial function to create the LoG kernel and imfilter for convolution. By utilizing these edge detection functions, researchers and engineers can effectively analyze and process edge information in digital images, with each algorithm offering distinct advantages for different application scenarios and image characteristics.