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- W162213400 abstract "Robust model fitting is important for computer vision tasks due to the occurrence of multiple model instances, and, unknown nature of noise. The linear errors-in-variables (EIV) model is frequently used in computer vision for model fitting tasks. This paper presents a novel formalism to solve the problem of robust model fitting using the linear EIV framework. We use Parzen windows to estimate the noise density and use a maximum likelihood approach for robust estimation of model parameters. Robustness of the algorithm results from the fact that density estimation helps us admit an a priori unknown multimodal density function and parameter estimation reduces to estimation of the density modes. We also propose a provably convergent iterative algorithm for this task. The algorithm increases the likelihood function at each iteration by solving a generalized eigenproblem. The performance of the proposed algorithm is empirically compared with Least Trimmed Squares(LTS) — a state-of-the-art robust estimation technique, and Total Least Squares(TLS) — the optimal estimator for additive white Gaussian noise. Results for model fitting on real range data are also provided." @default.
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- W162213400 date "2004-01-01" @default.
- W162213400 modified "2023-09-23" @default.
- W162213400 title "A Robust Nonparametric Estimation Framework for Implicit Image Models." @default.
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