Computer Vision Laboratory (CVL)

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

Autonoma system datorseendePhoto: 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 the Computer Vision Laboratory (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 given by the Division of Computer Vision Laboratory


Specialisation in Computer Vision and Signal Analysis


Master Thesis Projects in Computer Vision


Follow CVL on social media

Twitter @CvlIsy


Research within WASP Computer Vision Laboratory

Other research collaborations


Demo of autonomous vehicle in Visionen.

ISY Day 2024 – AI in Society, Education, and Research

This year’s edition of ISY Day offered lectures, discussions, and demonstrations within this year's theme "AI in Society, Education, and Research". A theme that is both current and highly relevant at the Department of Electrical Engineering.

Lasse – the teacher who is always seeking to improve

He came to Linköping as a student in the early 1980s. Some 40 years later, Lasse Alfredsson is still at LiU. A teacher with a drive to constantly seek to improve remains. And his reward, students’ Aha! moments, is hard to beat.

ELLIIT Joint Autonomous Systems Lab in Linköping and Lund showcasing autonomous robot.

The University Board on Study Visit to Visionen

In December 2023, the university board visited the Department of Electrical Engineering (ISY). In the research arena Visionen, examples were presented highlighting Linköping University's crucial role in technological and societal development.

The supercomputer Berzelius photographed with fisheye lens.

Swedish AI research gets more muscle

The supercomputer Berzelius was inaugurated in the spring of 2021, and was then Sweden's fastest supercomputer for AI. Yet, more power is needed to meet the needs of Swedish AI research.

Successful premiere for ISY's PhD Workshop

For the first time the Department of Electrical Engineering organized an all-day conference dedicated solely to the department's PhD-students. Afterwards the director of PhD studies, Mark Vesterbacka, was more than satisfied when he summed up the day

Portrait of Michael Felsberg with closed eyes.

Human vision – a challenge for AI

Achieving diversity in human vision is one of the major challenges for AI research. In the vast majority of cases, we are better than machines at understanding the world around us. But machines are catching up – slowly but surely.

Latest publications


Johan Edstedt, Georg Bökman, Mårten Wadenbäck, Michael Felsberg (2024) DeDoDe: Detect, Don't Describe - Describe, Don't Detect for Local Feature Matching 2024 International Conference on 3D Vision (3DV) Continue to DOI
Shahina Kunhimon, Abdelrahman Shaker, Muzammal Naseer, Salman Khan, Fahad Khan (2024) Learnable weight initialization for volumetric medical image segmentation Artificial Intelligence in Medicine, Vol. 151, Article 102863 Continue to DOI
Tao Bai, Yiming Cao, Yonghao Xu, Bihan Wen (2024) Stealthy Adversarial Examples for Semantic Segmentation in Remote Sensing IEEE Transactions on Geoscience and Remote Sensing, Vol. 62, Article 5614817 Continue to DOI
Yichu Xu, Yonghao Xu, Hongzan Jiao, Zhi Gao, Lefei Zhang (2024) S³ANet: Spatial-Spectral Self-Attention Learning Network for Defending Against Adversarial Attacks in Hyperspectral Image Classification IEEE Transactions on Geoscience and Remote Sensing, Vol. 62, Article 5512913 Continue to DOI
Mubashir Noman, Mustansar Fiaz, Hisham Cholakkal, Salman Khan, Fahad Khan (2024) ELGC-Net: Efficient Local- Global Context Aggregation for Remote Sensing Change Detection IEEE Transactions on Geoscience and Remote Sensing, Vol. 62, Article 4701611 Continue to DOI

Staff at the Computer Vision Laboratory

About the Department