This paper addresses the issue of motion estimation on image sequences. The standard motion equation used to compute the apparent motion of image irradiance patterns is an invariance brightness based hypothesis called the optical flow constraint. Other equations can be used, in particular the extended optical flow constraint, which is a variant of the optical flow constraint, inspired by the fluid mechanic mass conservation principle. In this paper, we propose a physical interpretation of this extended optical flow equation and a new model unifying the optical flow and the extended optical flow constraints. We present results obtained for synthetic and meteorological images. 1.

This paper presents an approach to measuring fluid flow from image sequences. The approach centers around a motion-recovery algorithm that is based on principles from fluid mechanics: The algorithm is constrained so that recovered flows observe conservation of mass as well as physically motivated boundary conditions. Empirical results from application of the algorithm to transmittance imagery of fluid flows, where the fluids contained a contrast medium, are presented. In these experiments, the algorithm recovered accurate and precise estimates of the flow. The significance of this work is twofold. First, from a theoretical point of view it is shown how information derived from the physical behavior of fluids can be used to motivate a flow-recovery algorithm. Second, from an applications point of view the developed algorithm can be used to augment the tools that are available for the measurement of fluid dynamics; other imaged flows that observe compatible constraints might benefit in a similar fashion. c ○ 2000 Academic Press 1.1. Motivation 1.

In this paper we address the problem of estimating and analyzing the motion in image sequences showing fluid phenomenon. Due to the great deal of spatial and temporal distortions that luminance patterns exhibit in images of fluid, standard techniques from Computer Vision, originally designed for quasi-rigid motions with stable salient features, are not well adapted in this context. In that prospect, we investigate a dedicated energybased motion estimator. The considered functional includes an original data model relying on the continuity equation of fluid mechanics. This new data model, which is specifically designed to be embedded in a multiresolution framework, is associated to an original divcurl type regularization. The optimization of the global energy function is solved within an efficient multigrid scheme. The performances of the resulting fluid flow estimator are demonstrated both on synthetic and real (meteorological) image sequences.

In this paper, several important issues related to visual motion analysis are addressed with a focus on the type of motion information to be estimated and the way contextual information is expressed and exploited. Assumptions (i.e., data models) must be formulated to relate the observed image intensities with motion, and other constraints (i.e., motion models) must be added to solve problems like motion segmentation, optical flow computation, or motion recognition. The motion models are supposed to capture known, expected or learned properties of the motion field, and this implies to somehow introduce spatial coherence or more generally contextual information. The latter can be formalized in a probabilistic way with local conditional densities as in Markov models. It can also rely on predefined spatial supports (e.g., blocks or pre-segmented regions). The classic mathematical expressions associated with the visual motion information are of two types. Some are continuous variables to represent velocity vectors or parametric motion models. The other are discrete variables or symbolic labels to code motion detection output (binary labels) or motion segmentation output (numbers of the motion regions or layers). We introduce new models, called mixed-state auto-models, whose variables belong to a domain formed by the union of discrete and continuous values, and which include local spatial contextual information. We describe how such...

This article describes a methodology for analyzing the Lagrangian structure of fluid flows in meteorological multispectral image sequences. Following a scale-space approach, we first construct a non-ponctual robust estimator for the locally dominant orientation field in the image. Based on the structure tensor, this estimator is relevant in both mono- and multispectral contexts. The Lagrangian component of the flow is then estimated by fitting a hierarchical vector parametric model to the dominant orientation field. Within this framework, we introduce a novel variational approach allowing a joint optimization of model parameters and support. A structural characterization of the resulting vector field is finally derived by means of classical di#erential geometry techniques. This methodology is applied to the analysis of temperated latitude depressions in Meteosat images. 1 Introduction L'analyse d'ecoulements fluides dans des sequences d'images est une problematique recurrente dans de ...

We propose to incorporate a priori geometric constraints in a 3--D stereo reconstruction scheme to cope with the many cases where image information alone is not sufficient to accurately recover 3--D shape. Our approach is based on the iterative deformation of a 3--D surface mesh to minimize an objective function. We show that combining anisotropic meshing with a nonquadratic approach to regularization enables us to obtain satisfactory reconstruction results using triangulations with few vertices. Structural or numerical constraints can then be added locally to the reconstruction process through a constrained optimization scheme. They improve the reconstruction results and enforce their consistency with a priori knowledge about object shape. The strong description and modeling properties of differential features make them useful tools that can be efficiently used as constraints for 3--D reconstruction. 1.

In this paper we review and present different methods for the detection and characterization of vortices. Our algorithm works on the segmentation of the image into affine layers. These layers are computed using a parametric tensor voting and encoded in an adjacency graph. Paths are computed from the adjacency graph and are used for characterizing paths' properties such as: critical points and vortices. We illustrate the proposed approach to a satellite image sequence of water vapor in the atmosphere.

The computation of the 2-D motion field from a sequence of images is one of the key tasks of many vision systems. Analysis and interpretation of flow fields is in general a complex task. One of the most interesting problems is to locate critical points in the motion field. The main theme of this thesis is the identification of critical points in motion fields, which have a physical significance for the corresponding application. The discrete Hodge Helmholtz decomposition is a vector decomposition algorithm which is used in this thesis for locating critical points in a motion field. Automatic