Discrete-time signal processing
Discrete-time signal processing
Digital signal processing (2nd ed.): principles, algorithms, and applications
Digital signal processing (2nd ed.): principles, algorithms, and applications
Performance of optical flow techniques
International Journal of Computer Vision
Computing Optical Flow with Physical Models of Brightness Variation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatio-Temporal Image Processing: Theory and Scientific Applications
Spatio-Temporal Image Processing: Theory and Scientific Applications
Optimally Rotation-Equivariant Directional Derivative Kernels
CAIP '97 Proceedings of the 7th International Conference on Computer Analysis of Images and Patterns
Numerische Isotropieoptimierung von FIR-Filtern mittels Querglättung
Mustererkennung 1997, 19. DAGM-Symposium
Variational Image Motion Computation: Theoretical Framework, Problems and Perspectives
Mustererkennung 2000, 22. DAGM-Symposium
Combining the Advantages of Local and Global Optic Flow Methods
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Optimally isotropic Laplacian operator
IEEE Transactions on Image Processing
Range Flow for Varying Illumination
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Divide-and-conquer strategies for estimating multiple transparent motions
IWCM'04 Proceedings of the 1st international conference on Complex motion
An adaptive confidence measure for optical flows based on linear subspace projections
Proceedings of the 29th DAGM conference on Pattern recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Complex motion models for simple optical flow estimation
Proceedings of the 32nd DAGM conference on Pattern recognition
On performance analysis of optical flow algorithms
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
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Estimation of optical flow and physically motivated brightness changes can be formulated as parameter estimation in linear models. Accuracy of this estimation heavily depends on the filter families used to implement the models. In this paper we focus on models whose terms are all data dependent and therefore are best estimated via total-least-squares (TLS) or similar estimators. Using three different linear models we derive model dependent optimality criteria based on transfer functions of filter families with given fixed size. Using a simple optimization procedure, we demonstrate typical properties of optimal filter sets for optical flow, simultaneous estimation of optical flow and diffusion, as well as optical flow and exponential decay. Exemplarily we show their performance and state some useful choices.