Globally Optimal Spatio-temporal Reconstruction from Cluttered Videos

International audienceWe propose a method for multi-view reconstruction from videos adapted to dynamic cluttered scenes under uncontrolled imaging conditions. Taking visibility into account, and being based on a global optimization of a true spatio-temporal energy, it oilers several desirable properties: no need for silhouettes, robustness to noise, independent from any initialization, no heuristic force, reduced flickering results, etc. Results on real-world data proves the potential of what is, to our knowledge, the only globally optimal spatio-temporal multi-view reconstruction method

New technologies in surgery

The progress of technology and robotics in industry has not yield to an equivalent development in the medical field. This paper analyses surgical proce-dures from the point of view of industrial processes looking for analogies in both fields so as to evaluate the possibilities of using equivalent technologies in both of them. After analyzing surgical specialties from the mechanical point of view, the actions to be performed, and the main requirements as precision and working conditions, a look at the main challenges that surgical robotics should face is presented.Peer ReviewedPostprint (published version

Simultaneous Determination of Registration and Deformation Parameters among 3D Range Images

Conventional registration algorithms are mostly concerned with rigid-body transformation parameters between a pair of 3D range images. Our proposed framework aims to determine, in a unified manner, not only such rigid transformation parameters but also various deformation parameters, assuming that the deformation we handle here is strictly defined by some parameterized fomulation derived from the deformation mechanism. In this respect, our proposed framework is different from the deformation researched in such fields as medical imaging. Like other conventional registration algorithms, our algorithm is formulated as a minimization problem of the squared sum of the distance between the corresponding points among a pair of range images. While conventional registration algorithms usually minimize this sum concerned about six parameters (three translation and three rotation parameters), the evaluation function in our proposed algorithm includes deformation parameters as well. Our proposed algorithm can be applied to a wide range of areas of computer vision, in particular, shape modeling and shape analysis. In this paper, we describe how we formulated such an algorithm, implemented it, and evaluated its performance.