Using RANSAC for estimating geometric transforms in computer vision
Random sample consensus, or RANSAC, is an iterative method for estimating a mathematical model from a data set that contains outliers. The RANSAC algorithm works by identifying the outliers in a data set and estimating the desired model using data that does not contain outliers.
RANSAC is accomplished with the following steps
- Randomly selecting a subset of the data set
- Fitting a model to the selected subset
- Determining the number of outliers
- Repeating steps 1-3 for a prescribed number of iterations
For example, the equation of a line that best fits a set of points can be estimated using RANSAC.
In computer vision, RANSAC is used as a robust approach to estimate the fundamental matrix in stereo vision, for finding the commonality between two sets of points for feature-based object detection, and registering sequential video frames for video stabilization.
For details, see Computer Vision Toolbox, which is used with MATLAB and Simulink.
Examples and How To
- Estimate geometric transform from matching point pairs using RANSAC (Documentation)
- Estimate fundamental matrix from corresponding points in stereo images with RANSAC (Documentation)
- Feature Detection, Extraction, and Matching (Documentation)
See also: feature extraction, stereo vision, object detection, image recognition, object recognition, RANSAC videos, point cloud