Computer Graphics and Image Processing

The computer graphics and image processing group is driving a number of research projects directed towards the development of theory and methodology for image capture, image analysis and image synthesis.  

A common theme within our projects is to develop algorithms and techniques for measuring and digitizing real environments, lighting conditions and material properties so that this information can be used to simulate the interaction between light and matter in a scene to create photo-realistic computer graphics images.

With a strong foundation in theoretically oriented research, the group is active within a number of demonstrator projects and industrial and academic collaborations directed towards development of state-of-the-art applications within the focus areas. We are currently working with projects directed towards:

  • New algorithms and methodologies for photo realistic image synthesis based on Monte Carlo integration
  • High Dynamic Range (HDR) imaging and video capture and statistical image reconstruction
  • Tone mapping and compression of HDR images and video
  • Capture, processing and Light field imaging
  • Appearance capture and modelling of material properties for photo-realistic image synthesis
  • Algorithms and methodology for capture and reconstruction of lighting, geometry and material properties of real scenes based on sensor data


Latest publications


Milda Poceviciute, Gabriel Eilertsen, Claes Lundström (2024) Benefits of spatial uncertainty aggregation for segmentation in digital pathology Journal of Medical Imaging, Vol. 11 Continue to DOI


Jens Nilsson, Jonas Unger (2023) Swedish civil air traffic control dataset Data in Brief, Vol. 48, Article 109240 Continue to DOI
Milda Poceviciute, Gabriel Eilertsen, Claes Lundström (2023) Spatial uncertainty aggregation for false negatives detection in breast cancer metastases segmentation MEDICAL IMAGING 2023, Article 124710W Continue to DOI
Behnaz Kavoosighafi, Jeppe Revall Frisvad, Saghi Hajisharif, Jonas Unger, Ehsan Miandji (2023) SparseBTF: Sparse Representation Learning for Bidirectional Texture Functions
Milda Pocevičiūtė (2023) Generalisation and reliability of deep learning for digital pathology in a clinical setting
Pierangelo Dellacqua, Stefania Costantini (2023) Empathetic human-agent interaction via emotional behavior trees INTELLIGENZA ARTIFICIALE, Vol. 17, p. 89-100 Continue to DOI
Milda Pocevičiūtė, Gabriel Eilertsen, Stina Garvin, Claes Lundström (2023) Detecting Domain Shift in Multiple Instance Learning for Digital Pathology Using Fréchet Domain Distance Medical Image Computing and Computer Assisted Intervention – MICCAI 2023: 26th International Conference, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, Part V, p. 157-167 Continue to DOI
Maja Krivokuca, Ehsan Miandji, Christine Guillemot, Philip A. A. Chou (2023) Compression of Plenoptic Point Cloud Attributes Using 6-D Point Clouds and 6-D Transforms IEEE transactions on multimedia, Vol. 25, p. 593-607 Continue to DOI
Alex Knutsson, Jakob Unnebäck, Daniel Jönsson, Gabriel Eilertsen (2023) CDF-Based Importance Sampling and Visualization for Neural Network Training Eurographics Workshop on Visual Computing for Biology and Medicine Continue to DOI
Katerina Vrotsou, Carlo Navarra, Kostiantyn Kucher, Igor Fedorov, Fredrik Schück, Jonas Unger, Tina-Simone Neset (2023) Towards a Volunteered Geographic Information-Facilitated Visual Analytics Pipeline to Improve Impact-Based Weather Warning Systems Atmosphere, Vol. 14, Article 1141 Continue to DOI