Researcher

Anders Heyden

Also known as Anders Lars-Gunnar Heyden

Anders Heyden is a Lund University professor whose work connects computer vision, camera geometry, and machine learning. He studied engineering physics and mathematics at Lund, then developed methods for uncalibrated image sequences and hand-eye calibration. His career included leadership in image-analysis and pattern-recognition societies, an IAPR fellowship in 2016, and three co-founded startups. As of October 2026, he is a professor of computer vision and machine learning and deputy dean at Lund; his recent work applies deep learning to medical imaging.

Works
309
Citations
10,811
h-index
32

Publication numbers from OpenAlex, as of 18 October 2026.

Timeline

  1. 1989–1995

    Engineering physics and mathematics

    He earns an M.Sc. in engineering physics in 1989 and a Ph.D. in mathematics in 1995 at Lund University.

  2. 1997

    Multilinear forms for image sequences

    His 1997 paper with Karl Åström sets out a mathematical framework for uncalibrated camera sequences and algorithms to recover structure and motion.

  3. 2000

    Hand-eye calibration

    He and Henrik Malm present “Hand-eye calibration from Image Derivatives” at the European Conference on Computer Vision in 2000.

  4. 2010–2018

    Society leadership at Lund and IAPR

    He serves as president of the Swedish Society for Automated Image Analysis in 2010–2014 and on the IAPR governing board in 2010–2018.

  5. 2016

    An IAPR fellowship

    He becomes an IAPR Fellow in 2016 for contributions to multiple view geometry and auto-calibration and for service to IAPR.

  6. 2023

    A startup innovation award

    He co-founds Ludesi, Mometric, and 23°N; the last startup wins the Sparbanken Skåne Innovation Award in 2023.

  7. 2025–2026

    Deep learning in medical imaging

    In 2025, he co-authors a deep-learning framework to predict outcomes for prostate-cancer patients on active surveillance. In 2026, he co-authors a study of deep-learning segmentation of brain tissues with dual-energy CT.

  8. As of October 2026

    Professor and deputy dean

    He is professor of computer vision and machine learning at Lund University, after postdoctoral research and associate-professor roles at its Centre for Mathematical Sciences. He is deputy dean of the engineering faculty with responsibility for research and research education.

Career

  1. Since 1994Lund UniversityPostdoctoral researcher
  2. From 2023LudesiCo-founder
  3. From 2023MometricCo-founder
  4. From 202323°NCo-founder
  5. 2010–2014Swedish Society for Automated Image AnalysisPresident
  6. 2010–2018International Association for Pattern RecognitionGoverning board member
  7. 2002–2009Malmö University

Education

  1. 1995Lund UniversityPh.D. in mathematics
  2. 1989Lund UniversityM.Sc. in engineering physics

Most-cited works

  1. Multiple view geometry2005 · 1,124 citationsWith Marc Pollefeys
  2. Computer Vision - Eccv 20022008 · 1,077 citations
  3. Euclidean reconstruction from image sequences with varying and unknown focal length and principal point2002 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 210 citations
  4. Three‐dimensional biofilm model with individual cells and continuum EPS matrix2006 · Biotechnology and Bioengineering · 198 citations
  5. Euclidean reconstruction from constant intrinsic parameters1996 · International Conference on Pattern Recognition · 178 citations

Sources and credits

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  1. Anders Heyden - Lund University portal.research.lu.se. Checked 18 October 2026.
  2. Simplifications of Multilinear forms for Sequences of Images - Lund University portal.research.lu.se. Checked 18 October 2026.
  3. Hand-eye calibration from Image Derivatives - Lund University portal.research.lu.se. Checked 18 October 2026.
  4. SSBA:s historia – SSBA ssba.org.se. Checked 18 October 2026.
  5. Chronological List of IAPR Fellows – International Association for Pattern Recognition iapr.org. Checked 18 October 2026.
  6. Outcome prediction of prostate cancer patients on active surveillance using weakly supervised deep learning - Lund University portal.research.lu.se. Checked 18 October 2026.
  7. Dual energy CT and deep learning for an automated volumetric segmentation of the major intracranial tissues: Feasibility and initial findings - Lund University portal.research.lu.se. Checked 18 October 2026.