Mines ParisTech

About: Mines ParisTech is a education organization based out in Paris, France. It is known for research contribution in the topics: Finite element method & Microstructure. The organization has 6564 authors who have published 11676 publications receiving 359898 citations. The organization is also known as: École nationale supérieure des mines de Paris & École des mines de Paris.

Detection, segmentation and classification of 3D urban objects using mathematical morphology and supervised learning

Abstract: We propose an automatic and robust approach to detect, segment and classify urban objects from 3D point clouds. Processing is carried out using elevation images and the result is reprojected onto the 3D point cloud. First, the ground is segmented and objects are detected as discontinuities on the ground. Then, connected objects are segmented using a watershed approach. Finally, objects are classified using SVM with geometrical and contextual features. Our methodology is evaluated on databases from Ohio (USA) and Paris (France). In the former, our method detects 98% of the objects, 78% of them are correctly segmented and 82% of the well-segmented objects are correctly classified. In the latter, our method leads to an improvement of about 15% on the classification step with respect to previous works. Quantitative results prove that our method not only provides a good performance but is also faster than other works reported in the literature.

Grain refinement in AZ91 magnesium alloy during thermomechanical processing

Abstract: Microstructural changes during high-temperature extrusion and torsion of an AZ91 alloy (Mg–9Al–1Zn, wt.%) were investigated. In the experimental domain studied, dynamic recrystallisation (DRX) occurs and the effect of temperature and strain rate on the resulting recrystallised grain size was investigated. Complete recrystallisation in torsion is associated with the development of a stress plateau after softening from the peak stress, which is systematically observed in the first steps of straining. The resulting grain size can be related to the value of the peak stress. It appears that the precipitation of the Mg 17 Al 12 phase does not affect significantly the torsion behaviour of the alloy in the experimental domain investigated here. This study supports the idea that very fine-scale microstructures (i.e. with a mean grain size smaller than 5 μm) can be easily produced by DRX during high-temperature extrusion of the AZ91 alloy.

Consistency and Convergence Rates of One-Class SVMs and Related Algorithms

Abstract: We determine the asymptotic behaviour of the function computed by support vector machines (SVM) and related algorithms that minimize a regularized empirical convex loss function in the reproducing kernel Hilbert space of the Gaussian RBF kernel, in the situation where the number of examples tends to infinity, the bandwidth of the Gaussian kernel tends to 0, and the regularization parameter is held fixed. Non-asymptotic convergence bounds to this limit in the L2 sense are provided, together with upper bounds on the classification error that is shown to converge to the Bayes risk, therefore proving the Bayes-consistency of a variety of methods although the regularization term does not vanish. These results are particularly relevant to the one-class SVM, for which the regularization can not vanish by construction, and which is shown for the first time to be a consistent density level set estimator.