Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Determination of optical flow and its discontinuities using non-linear diffusion
ECCV '94 Proceedings of the third European conference on Computer Vision (Vol. II)
Direct incremental model-based image motion segmentation for video analysis
Signal Processing - Video segmentation for content-based processing manipulation
Reliable Estimation of Dense Optical Flow Fields with Large Displacements
International Journal of Computer Vision
Variational Optic Flow Computation with a Spatio-Temporal Smoothness Constraint
Journal of Mathematical Imaging and Vision
Inverse Problem Theory and Methods for Model Parameter Estimation
Inverse Problem Theory and Methods for Model Parameter Estimation
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
Image assimilation for motion estimation of atmospheric layers with shallow-water model
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
A variational approach for object contour tracking
VLSM'05 Proceedings of the Third international conference on Variational, Geometric, and Level Set Methods in Computer Vision
Recovering missing data on satellite images
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Divergence-free motion estimation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
| Hi-index | 0.00 |
Data Assimilation is a mathematical framework used in environmental sciences to improve forecasts performed by meteorological, oceanographic or air quality simulation models. It aims to solve an evolution equation, describing the temporal dynamics, and an observation equation, linking the state vector and observations. In this article we use this framework to study a class of ill-posed Image Processing problems, usually solved by spatial and temporal regularization techniques. An approach is proposed to convert an ill-posed Image Processing problem in terms of a Data Assimilation system, solved by a 4D-Var method. This is illustrated by the estimation of optical flow from a noisy image sequence, with the dynamic model ensuring the temporal regularity of the result. The innovation of the paper concerns first, the extensive description of the tasks to be achieved for going from an image processing problem to a data assimilation description; second, the theoretical analysis of the covariance matrices involved in the algorithm; and third a specific discretisation scheme ensuring the stability of computation for the application on optical flow estimation.