Computer Vision and Learning Systems (CVL)

Welcome to Computer Vision and Learning systems (CVL), part of the Department of Electrical engineering at Linköping University.

Robot and researcher.Foto: Göran Billeson

The field of computer vision is a sub area of AI, and it has its roots in the modeling of the human visual system (HVS).

It is commonly accepted that about 80% of what we perceive is vision-based (DOI 10.3233/NRE-2010-0599), but modeling vision is a systematically underestimated scientific challenge - an implication of Moravec’s paradox, “We're least aware of what our minds do best” (Minsky 1986).

The highly intuitive nature of the HVS makes it difficult for us to understand the myriad of interdisciplinary problems associated with computer vision.

The research at Computer Vision and Learning Systems (CVL) has a strong focus on theory and methods, in particular within machine learning, signal processing, and applied mathematics. The resulting methods are applied in fields where technical systems are supposed to coexist with and therefore predict actions of humans. Self-driving cars sharing road space and interacting with humans, sustainable forestry and agriculture, monitoring of greenhouse gases as well as classification and monitoring of animals are some application domains.

CVL's research topics cover a wide range of challenges within machine learning for computer vision and robot perception:

  • Continuous-time modeling of 3D motion
  • Estimation of pose and 3D structure
  • Few-shot and weakly supervised learning
  • Geometric deep learning
  • Human and animal motion analysis
  • Medical imaging and analysis
  • Quantum machine learning
  • Reinforcement learning
  • Remote sensing and data analysis
  • Semi-supervised and incremental learning
  • Scene flow estimation
  • Uncertainty representation
  • Video and semantic segmentation
  • Vision for action

'He who loves practice without theory is like the sailor who boards ship without a rudder and compass and never knows where he may cast.'
Leonardo da Vinci (1452-1519)

Courses

Courses given by the Computer Vision and Learning Systsems

Specialisations

Specialisation in Computer Vision and Signal Analysis

Specialisation in Mathematical Computer Vision

Thesis

Master Thesis Projects in Computer Vision and Learning Systems

Contact

Research within WASP Computer Vision Laboratory

Other research collaborations

News

Två små statyer av människor står på ett bord.

22 June 2026

PhD Workshop 2026 brought together doctoral students from across ISY

For the third time, the Department of Electrical Engineering (ISY) recently hosted its PhD Workshop. During the day, PhD students, senior researchers and industry representatives gathered to take part in a total of 30 presentations.

A group of remote controlled devices sitting on top of a dirt field.

04 June 2026

CHASS recruits PhD students for research on next-generation drone swarms

CHASS, the Center for Heterogeneous Adaptive Swarm Systems, is now recruiting PhD students for research that could contribute to future search and rescue operations, environmental monitoring and the protection of critical infrastructure.

En man i kostym och glasögon står framför en byggnad.

16 April 2026

PhD student profile Ziliang Xiong

Ziliang Xiong is pursuing a PhD in Electrical Engineering, specializing in Computer Vision, and is currently in his fourth year. During his doctoral studies, his focus has been on enhancing the safety and reliability of autonomous systems.

Latest publications

2026

Carl Hoffstedt, Per-Erik Forssén, Anton Wiberg (2026) Semantic annotation of 3D point clouds via label transfer from BIM models for AEC applications Results in Engineering (RINENG), Vol. 30, Article 110796 (Article in journal) https://dx.doi.org/10.1016/j.rineng.2026.110796
Tahereh Dehdarirad, Gabriel Eilertsen, Ericka Johnson, Saghi Hajisharif (2026) Individually fair representation learning for DINOv2 Discover Artificial Intelligence, Vol. 6, Article 515 (Article in journal) https://dx.doi.org/10.1007/s44163-026-01490-y
Senmao Li, Joost van de Weijer, Taihang Hu, Fahad Khan, Qibin Hou, Yaxing Wang, Jian Yang, Ming-Ming Cheng (2026) StyleDiffusion: Prompt-Embedding Inversion for Text-Based Editing Computational Visual Media, Vol. 12, p. 743-763 (Article in journal) https://dx.doi.org/10.26599/CVM.2025.9450462
Johannes Hägerlind, Bao-Long Tran, Urs Nathanael Waldmann, Per-Erik Forssén (2026) Robust Multi-view Self-Calibration from Dense Matches Proceedings of the 21st International Conference on Computer Vision Theory and Applications, p. 307-318 (Conference paper) https://dx.doi.org/10.5220/0014253000004084
Maria Magnusson, Michael Sandborg, Åsa Carlsson Tedgren, Alexandr Malusek (2026) Quantitative determination of gadolinium and iodine contrast agents in dual-energy computed tomography via a dual-energy iterative reconstruction algorithm: a simulation study on multi-contrast imaging Radiation Protection Dosimetry, Vol. 202, p. 268-275 (Article in journal) https://dx.doi.org/10.1093/rpd/ncaf163

Staff at the Computer Vision and Learning Systems

About the Department