Variational Optic Flow Computation with a Spatio-Temporal Smoothness Constraint
Journal of Mathematical Imaging and Vision
3D finite difference computation on GPUs using CUDA
Proceedings of 2nd Workshop on General Purpose Processing on Graphics Processing Units
A duality based approach for realtime TV-L1 optical flow
Proceedings of the 29th DAGM conference on Pattern recognition
From box filtering to fast explicit diffusion
Proceedings of the 32nd DAGM conference on Pattern recognition
TV-L1 optical flow for vector valued images
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Efficient two-dimensional smoothing with P-spline ANOVA mixed models and nested bases
Computational Statistics & Data Analysis
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With continually increasing data sizes, the relevance of the big n problem of classical likelihood approaches is greater than ever. The functional mixed-effects model is a well established class of models for analyzing functional data. Spatial functional data in a mixed-effects setting is considered, and so-called operator approximations for doing inference in the resulting models are presented. These approximations embed observations in function space, transferring likelihood calculations to the functional domain. The resulting approximated problems are naturally parallel and can be solved in linear time. An extremely efficient GPU implementation is presented, and the proposed methods are illustrated by conducting a classical statistical analysis of 2D chromatography data consisting of more than 140 million spatially correlated observation points.