The RANSAC (Random Sample Consensus) algorithm is widely used in computer vision for robust model fitting in the presence of noise or outliers. It works by iteratively selecting random subsets of data points and fitting a model to them.
For example, in tasks like image stitching or 3D reconstruction, RANSAC helps estimate transformations like homography or fundamental matrices while ignoring outliers.
Its ability to handle noisy data makes it essential for applications such as feature matching, motion estimation, and object detection in real-world scenarios.