Edge Detection in Images Using the Laplacian of Gaussian (LoG) Algorithm

This section provides a detailed explanation of the process for applying the Laplacian of Gaussian (LoG) algorithm for edge detection in images. The LoG algorithm operates by calculating the second-order derivatives of grayscale values to detect edges through zero-crossings. Implementation typically begins with smoothing the input image using a Gaussian filter to reduce noise sensitivity. The filtered image then undergoes Laplacian operator application to compute second-order derivatives, highlighting regions of rapid intensity change. Zero-crossings in the resulting LoG response indicate potential edge locations, where the derivative changes sign. In code, this can be achieved by combining Gaussian blur (e.g., using OpenCV's GaussianBlur() function) with Laplacian convolution (via Laplacian() function), followed by zero-crossing detection through scanning the output matrix for sign changes between adjacent pixels. Thresholding may be applied to suppress weak edges. This method enhances edge detection accuracy by mitigating noise interference while precisely localizing edges through mathematical derivative analysis, significantly improving image processing outcomes.