Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Reliable and Efficient Computation of Optical Flow
International Journal of Computer Vision
Image Sequence Analysis via Partial Differential Equations
Journal of Mathematical Imaging and Vision
International Journal of Computer Vision
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
Hierarchical Estimation and Segmentation of Dense Motion Fields
International Journal of Computer Vision
Multimodal Estimation of Discontinuous Optical Flow using Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Acquisition of Symbolic Description from Flow Fields: A New Approach Based on a Fluid Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Variational Approach to Multi-Modal Image Matching
VLSM '01 Proceedings of the IEEE Workshop on Variational and Level Set Methods (VLSM'01)
Entropy Estimation and Multiscale Processing in Meteorological Satellite Images
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods
International Journal of Computer Vision
Discrete Orthogonal Decomposition and Variational Fluid Flow Estimation
Journal of Mathematical Imaging and Vision
Infinitely Divisible Cascades to Model the Statistics of Natural Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Stochastic Filtering Technique for Fluid Flow Velocity Fields Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Theory of Critical Phenomena: An Introduction to the Renormalization Group
The Theory of Critical Phenomena: An Introduction to the Renormalization Group
Beyond pixels: exploring new representations and applications for motion analysis
Beyond pixels: exploring new representations and applications for motion analysis
A fluid motion estimator for schlieren image velocimetry
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Vortex and source particles for fluid motion estimation
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
Reconstructing images from their most singular fractal manifold
IEEE Transactions on Image Processing
A phase-based approach to the estimation of the optical flow field using spatial filtering
IEEE Transactions on Neural Networks
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Motion analysis of complex signals is a particularly important and difficult topic, as classical Computer Vision and Image Processing methodologies, either based on some extended conservation hypothesis or regularity conditions, may show their inherent limitations. An important example of such signals are those coming from the remote sensing of the oceans. In those signals, the inherent complexities of the acquired phenomenon (a fluid in the regime of fully developed turbulence-FDT) are made even more fraught through the alterations coming from the acquisition process (sun glint, haze, missing data etc.). The importance of understanding and computing vector fields associated to motion in the oceans or in the atmosphere (e.g.: cloud motion) raises some fundamental questions and the need for derivating motion analysis and understanding algorithms that match the physical characteristics of the acquired signals. Among these questions, one of the most fundamental is to understand what classical methodologies (e.g.: such as the various implementations of the optical flow) are missing, and how their drawbacks can be mitigated. In this paper, we show that the fundamental problem of motion evaluation in complex and turbulent acquisitions can be tackled using new multiscale characterizations of transition fronts. The use of appropriate paradigms coming from Statistical Physics can be combined with some specific Signal Processing evaluation of the microcanonical cascade associated to turbulence. This leads to radically new methods for computing motion fields in these signals. These methods are first assessed on the results of a 3D oceanic circulation model, and then applied on real data.