Jean-Marc Vézien

Integration of geometric elements, Euclidean relations, and motion curves for parametric shape and motion estimation

This paper presents an approach to shape and motion estimation that integrates heterogeneous knowledge into a unique model-based framework. We describe the observed scenes in terms of structured geometric elements (points, line segments, rectangles, 3D corners) sharing explicitly Euclidean relationships (orthogonality, parallelism, colinearity, coplanarity). Camera trajectories are represented with adaptative models which account for the regularity of usual camera motions. Two different strategies of automatic model building lead us to reduced models for shape and motion estimation with a minimal number of parameters. These models increase the robustness to noise and occlusions, improve the reconstruction, and provide a high-level representation of the observed scene. The parameters are optimally computed within a sequential Bayesian estimation procedure that gives accurate and reliable results on synthetic and real video imagery.

Motion Curves for Parametric Shape and Motion Estimation

This paper presents a novel approach to camera motion parametrization for the structure and motion problem. In a model-based framework, the hypothesis of (relatively) continuous and smooth sensor motion enables to reformulate the motion recovery problem as a global curve estimation problem on the camera path. Curves of incremental complexity are fitted using model selection to take into account incoming image data. No first estimate guess is needed. The use of modeling curves lead to a meaningful description of the camera trajectories, with a drastic reduction in the number of degrees of freedom. In order to characterize the behaviour and performances of the approach, experiments with various long video sequences, both synthetic and real, are undertaken. Several candidate curve models for motion estimation are presented and compared, and the results validate the work in terms of reconstruction accuracy, noise robustness and model compacity.