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

Courses given by the Division of Computer Vision Laboratory

Specialisations

Specialisation in Computer Vision and Signal Analysis

Thesis

Master Thesis Projects in Computer Vision

Follow CVL on social media

Twitter @CvlIsy

Contact

Research within WASP Computer Vision Laboratory

Other research collaborations

News

Tomas Landelius and Carolina Natel de Moura.

The focus period resulted in new collaborations for the climate

In the fall of 2024, researchers from around the world once again gathered at Linköping University for ELLIIT's five-week focus period. This time, the goal was to initiate and deepen collaborations in climate research using machine learning.

Participants are listening to a lecture.

Symposium aiming to improve the climate

In the fall of 2024, Linköping University once again hosted ELLIIT's five-week-long focus period. This guest researcher program aimed for greater breadth in interdisciplinarity this year, with the theme of machine learning for climate science.

Two men and a woman talk in front of a screen

Machine learning can give the climate a chance

Machine learning can help us discover new patterns and better tackle the climate crisis. Researchers from all over the world meet at Linköping University with the goal of finding and deepening collaborations in this area.

Latest publications

2025

Long Li, Nian Liu, Dingwen Zhang, Zhongyu Li, Salman Khan, Rao Anwer, Hisham Cholakkal, Junwei Han, Fahad Khan (2025) CONDA: Condensed Deep Association Learning for Co-salient Object Detection COMPUTER VISION - ECCV 2024, PT L, p. 287-303 (Conference paper) Continue to DOI
William Ljungbergh, Adam Tonderski, Joakim Johnander, Holger Caesar, Kalle Astrom, Michael Felsberg, Christoffer Petersson (2025) NeuroNCAP: Photorealistic Closed-Loop Safety Testing for Autonomous Driving COMPUTER VISION - ECCV 2024, PT XXX, p. 161-177 (Conference paper) Continue to DOI
Jin Zhang, Ruiheng Zhang, Yanjiao Shi, Zhe Cao, Nian Liu, Fahad Khan (2025) Learning Camouflaged Object Detection from Noisy Pseudo Label COMPUTER VISION-ECCV 2024, PT I, p. 158-174 (Conference paper) Continue to DOI
Georg Bokman, Johan Edstedt, Michael Felsberg, Fredrik Kahl (2025) Affine Steerers for Structured Keypoint Description COMPUTER VISION - ECCV 2024, PT LXXXVI, p. 449-468 (Conference paper) Continue to DOI
Saeed Anwar, Muhammad Tahir, Chongyi Li, Ajmal Mian, Fahad Khan, Abdul Wahab Muzaffar (2025) Image colorization: A survey and dataset Information Fusion, Vol. 114, Article 102720 (Article in journal) Continue to DOI

Staff at the Computer Vision Laboratory

About the Department