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Errors-in-variables modeling in optical flow estimation

IEEE Transactions on Image Processing, 2001
Gradient-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
openaire   +2 more sources

Estimation of errors-in-variables models

Proceedings of the 27th IEEE Conference on Decision and Control, 2003
The 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 ...
openaire   +1 more source

Freeform surface topography model for ultraprecision turning under the influence of various errors

Journal of Manufacturing Processes, 2021
Tielin Shi, Jianping Xuan, Wenhao Du
exaly  

Effective estimation of nonlinear errors-in-variables models

Communications in Statistics - Simulation and Computation, 2023
Zhensheng Huang, Shuyu Meng, Ziyi Ye
openaire   +1 more source

Errors in Management of Head and Neck Tumors

Ca-A Cancer Journal for Clinicians, 1968
Richard Larson, Hugh Thomas
exaly  

Likelihood-based refinement. I. Irremovable model errors

Acta Crystallographica Section A: Foundations and Advances, 2002
Vladimir Y Lunin   +2 more
exaly  

Effects of positional errors in model-assisted and model-based estimation of growing stock volume

Remote Sensing of Environment, 2016
Juha Hyyppä   +2 more
exaly  

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