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

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.

Future space conference event image.

Yonghao Xu to Speak at Future Space Conference 2024

Yonghao Xu, Assistant Professor at the Computer Vision Laboratory (CVL), has been invited as a keynote speaker at the Future Space Conference 2024.

Picture of award ceremony.

PhD Workshop 2024 – A Successful Initiative

For the second time, the Department of Electrical Engineering (ISY) organized a full-day conference for its PhD students – PhD Workshop 2024. A successful initiative with a focus on presenting the department's research.

Latest publications

2024

Ziliang Xiong, Arvi Jonnarth, Abdelrahman Eldesokey, Joakim Johnander, Bastian Wandt, Per-Erik Forssén (2024) Hinge-Wasserstein: Estimating Multimodal Aleatoric Uncertainty in Regression Tasks IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), p. 3471-3480 (Conference paper) Continue to DOI
Weikang Yu, Yonghao Xu, Pedram Ghamisi (2024) Universal adversarial defense in remote sensing based on pre-trained denoising diffusion models International Journal of Applied Earth Observation and Geoinformation, Vol. 133, p. 104131-104131, Article 104131 (Article in journal) Continue to DOI
Johan Edstedt, Qiyu Sun, Georg Bökman, Mårten Wadenbäck, Michael Felsberg (2024) RoMa: Robust Dense Feature Matching 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), p. 19790-19800 (Conference paper) Continue to DOI
Arvi Jonnarth (2024) Learning Robot Vision under Insufficient Data
Jie Zhao, Johan Edstedt, Michael Felsberg, Dong Wang, Huchuan Lu (2024) Leveraging the Power of Data Augmentation for Transformer-based Tracking 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), p. 6455-6464 (Conference paper) Continue to DOI

Staff at the Computer Vision Laboratory

About the Department