Center for Interdisziplinary Research

Dana Ballard

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Department of Computer Sciences, University of Texas at Austin, Austin, Texas, U.S.A.
Email: dana@cs.utexas.edu
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Fellow of the ZiF research group "Competition and Priority Control in Mind and Brain: New Perspectives from Task-Driven Vision"


CV

Ballard's main research interest is in computational theories of the brain with emphasis on human vision. In 1985, inspired by the system developed by Ruzena Bajcsy, he and Chris Brown led a team that designed and built the first high speed binocular camera control system capable of simulating human eye movements in real-time. The system was mounted on a robotic arm that allowed it to move at one meter per second in a two meter radius workspace. This system led to an increased understanding of the role of tasks in models of vision as well as the role of eye movements in basic visual computations.

He is the co-author with Chris Brown of Computer Vision (Prentice Hall), 1982, the first text in the field, and author of An Introduction to Natural Computation (MIT Press 1999).

Currently he is focusing on pursuing research that addresses questions in human visuo-motor control by using modeling human behavior in virtual reality environments. In addition he is interested in models of the brain that relate to detailed neural codes.



Current Main Research Interests

Vision and attention ultimately direct action, and thus the ultimate understanding of these will necessarily incorporate descriptions of the communication between them. Thus we need to understand the movement system at an abstract level. Modeling the generation of human movement can be extraordinarily difficult owing to the complexity of the underlying musculoskeletal system. The dynamics equations are non-linear and have many degrees of freedom, making them all but intractable to solve directly. We have shown that this system can be greatly simplified if its movements are modeled as a set of basis movements that can be adapted to varying conditions. Command torques for movements can be read out by a dynamic model that is constrained to follow human movements as measured by a motion capture system. The resultant torques can be replayed in the model to reproduce the original movements provided they are augmented by linear stabilizing terms. The resultant system provides a novel method of modeling human dynamics and suggests a novel way of formulating and solving the general problem of controlling human movement.



Five selected publications with particular relevance to the ZiF Research Group



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