Abstract: The invention relates to a method for producing nanoparticles of at least one oxide of a transition metal selected from Ti, Zr, Hf, V, Nb and Ta, which are coated with amorphous carbon, wherein said method includes the following successive steps: (i) a liquid mixture containing as precursors at least one alkoxyde of the transition metal, an alcohol, and an acetic acid relative to the transition metal is prepared and diluted in water in order to form an aqueous solution, the precursors being present in the solution according to a molar ratio such that it prevents or sufficiently limits the formation of a sol so that the aqueous solution can be freeze-dried, and such that the transition metal, the carbon and the oxygen are present in a stoichiometric ratio according to which they are included in the nanoparticles; (ii) the aqueous solution is freeze-dried; (ii) the freeze-dried product obtained during the preceding step is submitted to pyrolysis under vacuum or in an inert atmosphere in order to obtain the nano

Abstract: A method for determining an optimal labeling of pixels in computer vision includes modeling an image by a graph having interior nodes and edges where each image point p is associated with a graph node, each pair of nearest neighbor points p, q is connected by a graph edge, each graph node p is associated with a singleton potential c(p), and each graph edge is associated with a pairwise potential function d(p,q). A label is randomly assigned to each point to initialize unary variables including an indicator function that indicates which label is assigned to which point and dual variables including height variables associated with each node p and label a, and balance variables associated with each edge (p,q) and label a. For each label, a new label c is selected, a capacitated graph is constructed and solved. The label selection divides the image into disjoint regions.

Abstract: A method for registering digitized images using Markov Random Fields (MRFs) includes providing a source image f and a target image g, defining a deformation grid of control points, defining a coordinate transformation as T ? ( x ) = x + ? p ? G ? ? ? ( ? x - p ? ) ? d u p , where x is a point on the source image, p is a position vector of control point p, dp is a displacement vector for each control point, up is a label for point p associated with displacement dp, and ?( ) is a weighting function for the displacement vector, defining an MRF energy functional to be minimized by T as E t = 1 ? G ? ? ? p ? G ? V p t ? ( u p ) + 1 ? E ? ? ? p , q ? E ? V pq ? ( u p , u q ) , wherein |G| is a number of control points, |E| is a number of pairs of neighboring control points on a neighborhood system, t is an iteration counter, and associating the MRF with a primary linear program and solving the primary linear program usi