[NotiAMCA] ENIEF 2019 - Invitation to contribute to the session: Image-based Computational Models (Modelos Computacionales Derivados de Imágenes)

[Antonio Orlando]

Dear Editor:

I would be very grateful if you could please post in NotiAMCA the
following advert.
Many thanks.
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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 - aorlando at herrera.unt.edu.ar