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Errors-in-variables modeling in optical flow estimation
IEEE Transactions on Image Processing, 2001Gradient-based optical flow estimation methods typically do not take into account errors in the spatial derivative estimates. The presence of these errors causes an errors-in-variables (EIV) problem. Moreover, the use of finite difference methods to calculate these derivatives ensures that the errors are strongly correlated between pixels.
Lydia Ng, Victor Solo
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Estimation of errors-in-variables models
Proceedings of the 27th IEEE Conference on Decision and Control, 2003The so-called errors-in-variables models pose serious problems to traditional statistical estimation because the Gaussian likelihood function, defined by the natural quadratic error measure, has a saddle point rather than a maximum. A discussion is presented of the estimation of such models, including the number of linear relations in them, based on ...
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Freeform surface topography model for ultraprecision turning under the influence of various errors
Journal of Manufacturing Processes, 2021Tielin Shi, Jianping Xuan, Wenhao Du
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Effective estimation of nonlinear errors-in-variables models
Communications in Statistics - Simulation and Computation, 2023Zhensheng Huang, Shuyu Meng, Ziyi Ye
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Direction of arrival estimation in the presence of model errors by signal subspace matching
Signal Processing, 2021Mati Wax, Amir Adler
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Errors in Management of Head and Neck Tumors
Ca-A Cancer Journal for Clinicians, 1968Richard Larson, Hugh Thomas
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Likelihood-based refinement. I. Irremovable model errors
Acta Crystallographica Section A: Foundations and Advances, 2002Vladimir Y Lunin +2 more
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Effects of positional errors in model-assisted and model-based estimation of growing stock volume
Remote Sensing of Environment, 2016Juha Hyyppä +2 more
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