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

News

Latest publications

2024

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 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) Continue to DOI
Tina-Simone Neset, Lotta Andersson, Magnus Matteo Edström, Katerina Vrotsou, Clara Greve Villaro, Carlo Navarra, Kostiantyn Kucher, Fredrik Schück, Caroline Rydholm, Jonas Unger, Björn-Ola Linnér (2024) AI för klimatanpassning: Hur kan nya digitala teknologier stödja klimatanpassning?
Amirhossein Ahmadian, Yifan Ding, Gabriel Eilertsen, Fredrik Lindsten (2024) Unsupervised Novelty Detection in Pretrained Representation Space with Locally Adapted Likelihood Ratio International Conference on Artificial Intelligence and Statistics 2024, Proceedings of Machine Learning Research
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

2023

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

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