Cornelia Fermüller is a research scientist in the UMIACS Computer Vision Laboratory.
Fermüller’s research is in the areas of computer vision and human vision, and she has written more than 35 articles in journals and 100 publications in refereed conferences and books. In her computer vision work, she has developed many computational models and implemented software solutions for applications in visual navigation and image processing. Fermüller's work on biological vision involves examining the computational constraints, building simulation models, and performing psychophysical experiments to understand the possible computational mechanisms explaining human motion and low-level signal perception.
Many of her studies have been investigating the computational principles underlying multiple view geometry and statistics, and she has discovered a number of basic computational principles in the analysis of visual motion and shape recovery. These include view-invariant texture descriptors, constraints on 3-D motion estimation, 3-D shape and image segmentation, insights on the effects of sensor design on motion estimation, and the findings of statistical bias in low-level processing.
Fermüller has applied these studies in a number of applications, including new imaging sensors for better motion and shape recovery, software for visual motion tasks in navigation and robotics, and various tasks of video computing, such as compression, video manipulation, and image-based rendering.
Her current research interests are centered around developing cognitive robotic systems that integrate, perception with action, reasoning and language. In ongoing projects she develops robots that recognize human manipulation activities and search for an object in a room.
She received a doctorate from the Technical University of Vienna, Austria in 1993 and an M.S. from the University of Technology, Graz, Austria in 1989, both in applied mathematics.
Manipulation actions, event-based vision, mid-level vision, motion analysis, texture, optical illusions
- UMD Grand Challenges Team Project Grant: Music Education for All through AI
- NSF: Emphasizing Explanation in AI Augmented String Instrumental Education
- NSF: NeuroPacNet: Accelerating Research on Neuromorphic Perception, Action, and Cognition
- NSF: Research Coordination Network: Cognitive Functions in the Learning of Symbolic Signals & Systems
- NSF CPS: MONA LISA—Monitoring and Assisting with Actions