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FLUid Image analysis and Description


This interdisciplinary project, that we have entitled FLUID, aims at studying and developing new methods for the estimation, the analysis and the description of complex fluid flows from image sequences. We propose to devise novel image processing and computer vision methods by using sound methodological frameworks incorporating suitable physical models accounting for the observed phenomena. This domain of research encompasses a wide range of difficult issues and thus will have a significant impact on several scientific and application domains including meteorology, oceanography, and flow visualization in applied fluid mechanics. The project gathers researchers from the fields of image analysis, computer science, applied mathematics, fluid mechanics and meteorology. The consortium is composed of three computer vision groups, a group involved in dynamic meteorology, a group specialized in the study of turbulent shear flows and a first rank company in the domain of flow visualization systems. The first objective of our proposal consists in studying novel and efficient methods to estimate and analyse fluid motions from image sequences. The highly deformable nature of the observed phenomena and the fact that theyare only partially observable raise difficult issues. Nevertheless, the huge amount of data available in the concerned domains (meteorology, oceanography, experimental fluid mechanics), and the mastering of generic mathematical frameworks to design sound image processing methods (variational methods, statistical models) will allow us to devise dedicated fluid motion image processing techniques integrating physical models/constraints derived from the laws of fluid mechanics. The second objective is to guarantee the applicability of the developed techniques to a large range of fluid visualization applications. To that end, two specific areas will be considered: meteorological applications and experimental fluid mechanics for industrial evaluation.

Convocatoria de propuestas

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Campus universitaire de beaulieu
35042 Rennes

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Participantes (5)