Motion analysis in oceanographic satellite images using multiscale methods and the energy cascade
Résumé
Motion analysis of complex signals is a particularly important and difficult topic, as classical Com- puter Vision and Image Processing methodologies, either based on some extented 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 Developped Turbulence) are made even more fraught through the alterations coming from the acquisition process (solar glint, haze, missing data etc.). The importance of understanding and computing vector fields asso- ciated to motion in the oceans or in the atmosphere (e.g. : cloud motion) raise some fundamental questions and the need for derivating motion analysis and understanding algorithms that match the physical characteristics of the acquired signals. Among those questions, one of the most fun- damental is to understand what classical methodologies (e.g. : such as the various imlementations of the optical flow) are missing, and how their drawbacks can be lowered. In this paper, we show that the fundamental problem of motion evaluation in complex and turbulent acquisitions can be tackled using new multiscale characterisations of transition fronts and the use of appropriate para- digms coming from Statistical Physics, 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.