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
Signal Processing for Computer Vision
Signal Processing for Computer Vision
Disparity from Monogenic Phase
Proceedings of the 24th DAGM Symposium on Pattern Recognition
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
Mixed OLS-TLS for the Estimation of Dynamic Processes with a Linear Source Term
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Estimation of Surface Flow and Net Heat Flux from Infrared Image Sequences
Journal of Mathematical Imaging and Vision
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Image sequence processing techniques are an essential tool for the experimental investigation of dynamical processes such as exchange, growth, and transport processes. These processes constitute much more complex motions than normally encountered in computer vision. In this paper, optical flow based motion analysis is extended into a generalized framework to estimate the motion field and the parameters of dynamic processes simultaneously. Examples from environmental physics and live sciences illustrate how this framework helps to tackles some key scientific questions that could not be solved without taking and analyzing image sequences.