Unsupervised amplitude and texture based classification of SAR images with multinomial latent model

Using simple first-order Arrhenius kinetics

In accordance with the absence of any detectable IFN-α production, the blocking of type I IFN signaling had no effect on TRAIL and CXCL10 expression induced by

REPUBLIQUE ALGERIENNE DEMOCRATIQUE ET POPULAIRE MINISTERE DE L’ENSEIGNEMENT SUPERIEUR ET DE LA

Analytical (GSCS) and numerical longitudinal strain (left) and stress (right) in both matrix and inclusion phases for interface thickness of 20 µm obtained for different

Cette opération se fait avec l’opérateur Insérer arc-plan entre Extension (Proc[M]) et < [Élément X] ; Etendre, par DECOMPOSITION GLOBALE (Greffe Additionnelle), [Élément

Modifying the initial powder mixture and the thermal treatment conditions produced materials having different microstructures (A, B1, B2 and B3) [2]. Electrochemistry was used to:

This paper describes a supervised classification method based on local features derived from a Gaussian mixture model (GMM) of the distribution of patches.. First classification

This paper will present classification results on simulated and real images using a non-Gaussian signal model, more adapted to the high resolution images and a geometrical definition