אירועים

A new robust estimation algorithm, the generalized projection based M-estimator (gpbM) is proposed. The algorithm is general and can handle heteroscedastic data where every point in the estimation have a different covariance. Does not require the user to specify any (scale) parameters, and can be applied to multiple linear constraints for single and multi-carrier problems. The gpbM has three distinct stages: scale estimation, robust model estimation and inlier/outlier dichotomy. The model estimation stage can be further optimized by using Grassmann manifold theory. For data containing multiple inlier structures the estimator iteratively determines one structure at a time. We present four homoscedastic and five heteroscedastic computer vision problems with single or multiple carriers.

Work done together with Sushil Mittal and Saket Anand.