Computing reliable solutions to ill-posed inverse problems is one of the most important tasks in imaging science. The aim of this minisymposium is to present new trends in computer vision, numerical analysis, optimization, shape analysis with their applications in the real world. The presentations will promote interactions of these new challenging methods in many fields of imaging science, such as science, medicine, engineering with particular focus on 3D reconstructions, medical imaging, fingerprints and barcode problems. The speakers span from industrial to academic mathematical community to highlight interdisciplinary aspects of imaging science and to share the latest developments in this field.

Modeling and Learning Deep Representations, in Theory and in Practice

Stefano Soatto (University of California, Los Angeles)

Case study in learning to understand: 3D face reconstruction

Ron Kimmel (Technion - Israel Institute of Technology)

A Multidisciplinary Approach to Personalized Design of Orthopaedic Implants and other Devices

Alberto Leardini (Laboratory of Movement Analysis and Functional-Clinical Evaluation of Prosthesis, Istituto Ortopedico Rizzoli, Bologna)

Pancreatic cancer identification strategy on CT images based on higher-order statistics

Stefania Marconi (Department of Civil Engineering and Architecture, University of Pavia)

How flexible is your scanner?

Sophia Bethany Coban (Centrum Wiskunde & Informatica, University of Manchester)

Classifying stroke using electrical impedance tomography

Samuli Siltanen (University of Helsinki)

Evolution of barcode technology: mathematical models for advanced decoding algorithms

Francesco Deppieri (DATALOGIC)

Image analysis and biometrics @ Fingerprints

Sara Soltani (Fingerprint Cards, R&D Algorithm Development)

Organizers:

Roberto Mecca (University of Bologna and University of Cambridge)

Giulia Scalet (Dept. Civil Engineering and Architecture, University of Pavia)

Federica Sciacchitano (Dept. Mathematics, University of Genoa)

Keywords:

computed tomography, computer vision, deep learning, image enhancement, image reconstruction, image registration, inverse problems, medical imaging