Robust Affine Motion Estimation in Joint Image Space using Tensor Voting (ICPR 2002)
Abstract
Robustness of parameter estimation relies on discriminating inliers from
outliers within the set of correspondences. In this paper, we present a method
using tensor voting to eliminate outliers and estimating affine transformation
parameters directly from covariance matrix of selected inliers without
additional parameter estimation processing. Our approach is based on the
representation of the correspondences in a decoupled joint image space and
the use of the metric associated with the affine transformation. We enforce the
metric property in a joint image space for tensor voting, detect several inlier
groups corresponding distinct affine motions and directly estimate affine
parameters from each set of inliers. The proposed approach is illustrated by a
set of challenging examples.