Dear Editor: I would be very grateful if you could please post in NotiAMCA the following advert. Many thanks. ------------------------------------------------------------------------------------------------------ Dear Colleagues: I would like to invite you to contribute to the session on 'Image-based Computational Models' (Modelos Computacionales Derivados de Imágenes - https://enief2019.amcaonline.org.ar/sesiones) of the XXIV Congress on Numerical Methods and Its Applications (https://enief2019.amcaonline.org.ar/). The congress will be held in Santa Fe, Argentina from November 5th to November 9th, 2019. Deadline for the submission of abstracts is April 26th, 2019. The minisymposium will be a forum for discussion on the current state-of-the-art in the field of physics-based modeling and on any theme that is related to it for applications in any area of computational science. Please, below you have a more detailed description of the aims and general topics of the minisymposium. I look forward to meeting you in Santa Fe. Yours Sincerely, Antonio Orlando, FACET, UNT-CONICET
Image-based Computational Models (Modelos Computacionales Derivados de Imágenes) The purpose of this minisymposium is to discuss contributions to the generation of realistic and accurate models from imaging modalities so to be suitable for physics-based simulations, such as FEM and CFD. Instances of such imaging techniques are computed tomography (CT), magnetic resonance imaging (MRI), micro-CT, Ultrasound, Electrical Impedance Tomography, Microwave Imaging, Optical Tomography, etc. The process of converting greyscale 3D image data to a discretized domain suitable for simulation is often arduous and fraught with errors. It faces different challenges due to the number of disciplines that involves. These range from image analysis to computational geometry, to mesh generation and inverse problems. In this minisymposium, we explore techniques for improving this image-to-simulation process. Topics of interest include, but are not limited to: • Computed tomography reconstruction techniques to reduce artifacts • Image segmentation, labeling, and part identification • Computer vision and machine learning approaches • Registration, Acquisition, and Compression • Surface/Volume reconstruction from point cloud • Geometric feature identification and detection • Domain discretization/mesh generation • Principal components analyses, independent components analyses • Data compression and model reduction • Algorithms and numerical methods for solving multi-physics problems on image data • Inverse problems for domain identification Session organizer: Antonio Orlando, FACET, UNT-CONICET - aorla...@herrera.unt.edu.ar -----<*>-----<*>-----<*>--.N.O.T.I.A.M.C.A.--<*>-----<*>-----<*>-----<*> Los mensajes son archivados en la pagina Web del AMCA http://www.amcaonline.org.ar/ -----<*>-----<*>-----<*>-----<*>-----<*>-----<*>-----<*>-----<*>-----<*>