Edge Detection Using Canny Algorithm

In image processing, a series of algorithms and techniques are typically employed to accurately detect straight lines in images. The Canny operator serves as a widely-used edge detection algorithm that effectively identifies edge information in images through a multi-stage process: Gaussian filtering for noise reduction, gradient calculation using Sobel operators, non-maximum suppression for edge thinning, and dual-threshold hysteresis for edge tracking. Following edge detection, we apply Sobel operator convolution to the detected edges to enhance their intensity and clarity—this involves computing gradient approximations in horizontal and vertical directions using 3x3 kernels. Finally, the Hough transform algorithm converts edge points from Cartesian coordinates to parameter space (rho-theta representation), enabling robust straight line extraction through peak detection in the accumulator array. By combining these algorithms, we achieve more precise and reliable straight line detection results with implementations typically involving OpenCV functions like cv2.Canny(), cv2.Sobel(), and cv2.HoughLines().