Photo of Jonas Unger

Jonas Unger

Professor

Research leader – Computer Graphics and Image Processing

Presentation

Professor Jonas Unger is leading the research efforts within Computer Graphics and Image Processing group in the division for Media and Information Technology at the department of Science and Technology. The vision of the group is to research and develop new theory and technology for computational imaging by fusing computer graphics,vision and sensors with human perception and machine learning to capture, digitize and reason about aspects of the world that have not been possible before. With a strong foundation in the theoretically oriented research, the group is active within a number of industrial and academic collaborations directed towards development of state-of-the-art applications ranging from 3D-reconstruction of scenes, photorealistic image synthesis and digitization of optical material properties to computer vision for heart surgery, AI driven diagnostics tools for medical applications, perceptual display algorithms, and software for autonomous systems such as self-driving cars and robot navigation.

A collage with examples of different technics used in computer graphics


Publications

2024

Behnaz Kavoosighafi, Saghi Hajisharif, Ehsan Miandji, Gabriel Baravdish, Wen Cao, Jonas Unger (2024) Deep SVBRDF Acquisition and Modelling: A Survey Computer graphics forum (Print), Vol. 43 (Article in journal) Continue to DOI
George Baravdish, Gabriel Eilertsen, Rym Jaroudi, Tomas Johansson, Lukáš Malý, Jonas Unger (2024) A Hybrid Sobolev Gradient Method for Learning NODEs Operations Research Forum, Vol. 5, p. 1-39, Article 91 (Article in journal) Continue to DOI
Ehsan Miandji, Tanaboon Tongbuasirilai, Saghi Hajisharif, Behnaz Kavoosighafi, Jonas Unger (2024) FROST-BRDF: A Fast and Robust Optimal Sampling Technique for BRDF Acquisition IEEE Transactions on Visualization and Computer Graphics, Vol. 30, p. 4390-4402 (Article in journal) Continue to DOI
Gabriel Eilertsen, Daniel Jönsson, Jonas Unger, Anders Ynnerman (2024) Model-invariant Weight Distribution Descriptors for Visual Exploration of Neural Networks en Masse EuroVis 2024 - Short Papers (Conference paper) Continue to DOI
Danhua Lei, Ehsan Miandji, Jonas Unger, Ingrid Hotz (2024) Sparse q-ball imaging towards efficient visual exploration of HARDI data Computer graphics forum (Print) (Article in journal) Continue to DOI

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