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Henrik Christensen
Kungl Tekniska Högskolan
Membership Number: 5
Address: NADA/CVAP-CAS, KTH, S-100 44 Stockholm, Sweden
Email: hic@nada.kth.se
Phone: +46 8 790 6792
Fax: +46 8 723 0302
URL: http://www.cas.kth.se

Biographical Sketch
Henrik I. Christensen (1962) is a full professor of computer science and the director of the Centre for Autonomous Systems, at the Royal Institute of Technology. He received M. Sc. EE and Ph. D. degrees from Aalborg University, Denmark in 1987 and 1989, respectively. He was a research associate and associate professor at Aalborg University, 1989-1996. In addition he has held visiting positions at Oak Ridge National Laboratory and University of Pennsylvania. Dr. Christensen has been involved in a significant number of EU projects including BR-3038-Vision as Process, EP-7108-Vision as Process II, and the TMR Networks SMART, CAMERA, and VIRGO. Dr. Christensen has published more than 120 papers in the areas of vision, robotics, control and artificial intelligence. He is an associate editor of IEEE Trans. of PAMI, Machine Vision and Applications, International Journal of Pattern Recognition and Artificial Intelligence, AI Magazine, Robotics and Autonomous Systems, and MIT Series on Autonomous Robots and Intelligent Agents. His primary research interests are in system integration, active vision, and hybrid dynamic systems.

Kungl Tekniska Högskolan (Royal Institute of Technology)
The Computational Vision and Active Perception (CVAP) group at KTH is doing research in computational vision, robotics and related problems in geometric modeling and computing. The group was formed in 1982 and has now almost 30 researchers, graduate students and visiting scientists.

Seeing Robots: A central research theme at CVAP is the development of a computer based seeing agent capable of using vision in its interaction with the environment, for e.g. manoeuvring, navigating, grasping and recognizing things. This work is being implemented as "seeing robots" and addresses basic issues on gaze control, attention, figure-ground segmentation, cue integration, and recognition, as well as systems and control issues.

A second theme that underpins much of this and other work concerns early vision processing and the computation of scene characteristics. Considerable efforts are being devoted to feature and structure extraction at multiple scales. One of the goals is to develop the notion of a visual-front-end, which can provide a first processing layer for the mentioned seeing agent.

Visual Navigation: Another central topic deals with the derivation geometric invariants and their use in deriving scene structure and in tasks such as visual navigation. Properties invariant under perspective and projective transformations can be used to establish image correspondence and to tracking. They also provide a means for model indexing in visual recognition. The latter problem is also studied using other approaches, e.g. based on appearance and also involving learning. The overall goal is to include also these techniques and capabilities in our long-term work on the seeing agent.

In addition to these efforts the group is also developing frameworks and computational environments for performing geometric reasoning and analysis as well as for representing geometric objects such as surfaces and volumes. This work has potential applications in computer vision but is also aimed at the study and teaching of geometry as such.


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