Extracting Lane Lines from Binarized Images Using Hough Transform

To detect lane lines from an image, a common computer vision approach involves first converting the image to a binary format where lane markings appear as white pixels against a black background. This binary image serves as input for the Hough transform algorithm, which excels at detecting geometric shapes like straight lines. The implementation typically involves these key steps: 1. Image binarization using thresholding techniques (e.g., Otsu's method or adaptive thresholding) 2. Applying edge detection (Canny edge detector) to enhance line features 3. Configuring Hough transform parameters (rho and theta resolution, minimum vote threshold) 4. Processing the Hough space accumulation matrix to identify peak values corresponding to lines In Hough space, each white pixel from the binary image gets mapped to multiple possible lines parameterized by their distance (ρ) from the origin and angle (θ). The transform accumulates votes in a parameter space, where local maxima indicate the presence of lines. These detected lines can then be filtered and processed to determine lane position, orientation, and curvature using techniques like line clustering and RANSAC for robust fitting. The OpenCV library provides essential functions for implementation: - cv2.threshold() for image binarization - cv2.HoughLines() or cv2.HoughLinesP() (Probabilistic Hough Transform) for line detection - Post-processing functions to filter and extrapolate lane lines based on geometric constraints