Automatic Model Selection by Modelling the Distribution of Residuals
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Motion Estimation Using Statistical Learning Theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parametric model-based motion segmentation using surface selection criterion
Computer Vision and Image Understanding
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This contribution presents a novel approach to the challenging problem of model selection in motion estimation from sequences of images. New light is cast on parametric models of local optical flow. These models give rise to parameter estimation problems with highly correlated errors in variables (EIV). Regression is hence performed by equilibrated total least squares. The authors suggest to adaptively select motion models by testing local empirical regression residuals to be in accordance with the probability distribution that is theoretically predicted by the EIV model. Motion estimation with residual-based model selection is examined on artificial sequences designed to test specifically for the properties of the model selection process. These simulations indicate a good performance in the exclusion of inappropriate models and yield promising results in model complexity control.