A method for reconstructing label images from a few projections, as motivated by electron microscopy Our aim is to produce a tessellation of space into small voxels and, based on only a few tomographic projections of an object, assign to each voxel a label that indicates one of the components of interest constituting the object. Traditional methods are not reliable in applications, such as electron microscopy in which (due to the damage by radiation) only a few projections are available. We postulate a low level prior knowledge regarding the underlying distribution of label images, and then directly estimate the label image based on the prior and the projections. We use a relatively efficient approximation to a global search for the optimal estimate. Copyright Springer Science  Business Media, LLC 2006

LocalConvexity Reinforcement for Scene Reconstruction from Sparse Point Clouds Several methods reconstruct surfaces from sparse point clouds that are estimated from images. Most of them build 3D Delaunay triangulation of the points and compute occupancy labeling of the tetrahedra thanks to visibility information and surface constraints. However their most notable errors are falselylabeled freespace tetrahedra. We present labeling corrections of these errors based on a new shape constraint: localconvexity. In the simplest case, this means that a freespace tetrahedron of the Delaunay is relabeled matter if its size is small enough and all its vertices are in matter tetrahedra. The allowed corrections are more important in the vertical direction than in the horizontal ones to take into account the anisotropy of usual scenes. In the experiments, our corrections improve the results of previous surface reconstruction methods applied to videos taken by a consumer 360 camera.