In this research we develop new variational methods for segmentation anddense 3D interpretation of temporal sequence of monocular images. The segmentation, based on motion, is carried out jointly with the 3D interpretation which consists of a dense estimate of relative depth and 3D motion, i.e. of the structure and 3D motion of the mobile objects of the visual field. The movement of these objects, supposed rigid, is considered relative to the camera, thus allowing the objects and the camera to have simultaneous movements. These methods have two common points: the data input are the spatio-temporal variations of the image sequence, and the minimization of these functional is carried out by evolution of active curves via Euler-Lagrange equations and level sets. We propose threemethods: (A) direct method, (b) 3D interpretation with segmentation by essential parameters, and (c) 3D interpretation with segmentation by image motion. They differ essentially by the variables from their functional andthe criterion on which is based the segmentation. Each one of these methodsis formulated according to a variational principle with a functional to minimize.

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