A (not so) deep look into image colourisation and shape segmentation

by Prof. Dr. Jiří Kosinka (University of Groningen)

From its humble origins approximately 60 years ago, computer vision has developed into a mature interdisciplinary field that tackles challenging problems revolving around obtaining high-level understanding from digital images, videos, shapes, etc. Recent deep learning advances have transformed the field, allowing for solving problems that were previously beyond reach. We will focus on two problems where deep learning comes to the rescue: segmenting (families of) 3D shapes and colouring greyscale images, with image vectorisation linking these two challenges.

Jiří Kosinka is an Associate Professor at the Bernoulli Institute of the Faculty of Science and Engineering, University of Groningen, where he leads the Scientific Visualization and Computer Graphics research group. His interests include topics in the area of visual computing, with particular emphasis on geometric modelling, computer graphics, and image vectorization. He currently serves as Associate Editor for two Elsevier journals, Computer Aided Design and Graphical Models. He has been involved in organising several conferences in his field, such as the SIAM Conference on Computational Geometric Design as programme co-chair in 2021 and conference co-chair in 2023. He has co-authored over 100 scientific publications and served on more than 40 international programme committees.

Other keynotes at our Spring Meeting 2024:

• Why you should work in AI for biodiversity? by Laurens Hogeweg (Intel)
• Bayesian Deep Learning for Safe Computer Vision by Dr. Matias Valdenegro Toro (University of Groningen)