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A Closed-Form Solution to Tensor Voting for Robust Parameter Estimation via Expectation-Maximization

We prove a closed-form solution to second-order Tensor Voting (TV), and employ the resulting structure-aware tensors in ExpectationMaximization (EM). Our new algorithm, aptly called EM-TV, is an efficient and robust method for parameter estimation. Quantitative comparison shows that our method performs better than the conventional second-order TV and other representative techniques in parameter estimation (e.g., fundamental matrix estimation). While its implementation is straightforward, EM-TV will be available as a downloadable software library.