This project is concerned with model estimation when the measurements are plagued by large quantities of outliers, i.e. measurements with very large errors. This problem arises frequently in computer vision applications.
Model estimation with moderate measurement noise is a well-studied problem and even very large problems can be solved accurately. For robust model estimation we have a very different picture where heuristic methods without performance guarantees are often used. In this project we develop new efficient algorithms for these types of problems.