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Personal profile

Research

Kalle Åström received his B.Sc in Mathematics in 1990, M.Sc. degree in Engineering Physics in 1991 and Ph.D. in Mathematics in 1996 form Lund University, Sweden. His thesis was awarded Best Nordic Ph.D. Thesis in pattern recognition and image analysis 1995-1996 at the Scandinavian Conference in Image Analysis, 1997. He has been a post-doctoral research fellow, associate professor and is now professor at the Centre for Mathematical Sciences, Lund University. His current research interests include stochastic analysis of low level vision, computer vision for autonomous guided vehicles, geometry and algebra of multiple views of points, curves and surfaces, structure from motion using images, sound, radio and other sensors, cognitive vision, handwriting-recognition and medical image analysis.
He is currently coordinating the profile area Natural and Artificial Cognition at Lund University and also the network AI Lund at Lund University. 

He is a member and board member of the Royal Swedish Physiographic Society and board member of the Swedish AI Society (SAIS). He has previously been an Associate Editor for the IEEE Transactions on Pattern Analysis and Machine Intelligence, president of the Swedish Society for Automated Image Analysis (1998-2002), member of the governing board of the International Association for Pattern Recognition (1998-2004), member of the scientific advisory board of STFI-Packforsk (2002-2005). He has co-founded three start-up companies Decuma (1999), Cognimatics (2003) and Spiideo (2012), Neuromathics (2015) and been involved in several other start-ups. He is the winner of Innovation Cup 1991, for his work on autonomous guided vehicles. Decuma was awarded the EU IST Grand Prize award in 2003, for innovative work on hand-writing recognition.

Teaching

Teachings include undergraduate and gradate courses in mathematics, image processing and computer vision.

UKÄ subject classification

  • Geometry
  • Computer Vision and Robotics (Autonomous Systems)

Free keywords

  • Computer vision
  • Invariants
  • Structure from motion

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