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

En man som står på en scen med en mikrofon.

23 June 2026

ISY Day 2026 focused on intercultural communication

On 16 June, the Department of Electrical Engineering (ISY) gathered for this year's ISY Day. The event provided colleagues across the department with an opportunity to exchange experiences and reflect on the theme of intercultural communication.

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.

Latest publications

2026

Mingming Xu, Jin Xu, Zhiru Yang, Wei Li, Yonghao Xu, Shiqing Wei, Shanwei Liu, Bo Du, Zengmao Wang (2026) Differentiable Clustering Graph Convolutional Network for Hyperspectral Unmixing: Methodology and Benchmark IEEE Transactions on Neural Networks and Learning Systems (Article in journal) https://dx.doi.org/10.1109/TNNLS.2026.3698485
Yuning Cui, Syed Waqas Zamir, Ming-Hsuan Yang, Alois Knoll, Fahad Khan, Salman Khan (2026) StarIR: Convolutional Image Restoration With Spatial-Frequency Fusion IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 48, p. 8216-8233 (Article in journal) https://dx.doi.org/10.1109/TPAMI.2026.3672465
Mårten Wadenbäck, Marcus Valtonen Örnhag, Johan Edstedt (2026) Radially Distorted Homographies, Revisited 2026 International Conference on 3D Vision (3DV), p. 52-62 (Conference paper) https://dx.doi.org/10.1109/3dv69130.2026.00012
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

Staff at the Computer Vision and Learning Systems

About the Department