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Dana Ballard (Professor)
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Contact:

Dana Ballard
CSB 721
Department of Computer Science
University of Rochester
Rochester, NY 14627

email: dana@cs.rochester.edu
home: http://www.cs.rochester.edu/~dana/
vision: http://www.rochester.edu/research/vision/people/Dana_H_Ballard

About:

Dana Ballard's main research interest is in computational theories of the brain with emphasis on human vision. In 1985 with Chris Brown, he led a team that designed and built a high speed binocular camera control system that is capable of simulating human eye movements. The system is mounted on a robotic arm that allows it to move at one meter per second in a two meter radius workspace. This system has led to an increased understanding of the role of behavior in vision. The theoretical aspects of that system were summarized in a paper ``Animate Vision,'' which received the Best Paper Award at the 1989 International Joint Conference on Artificial Intelligence. Dana is also interested in models of the brain that relate to detailed neural models.

Research Interests:


Projects:

Quasi-static Object Discovery (QsOD): We discover quasi-static objects, objects that are stationary during some interval of observation, across image sequences acquired by any number of completely uncalibrated cameras using only temporal (no spatial) information.

Virtual Human Vision: This project is concerned with understanding the management of visually guided behavior in the face of dynamic environments and multiple goals. This project has two aims. The first is to address engineering questions concerning the control of embodied agents. The second is to address the related question of how humans handle visuo-motor tasks.


Publications:

Dynamic Model of visual Recognition Predicts Neural Response Properties in the Visual Cortex (Rajesh P.N. Rao and Dana H. Ballard)
Natural Basis Functions and Topographic Memory for Face Recognition (Rajesh P.N. Rao and Dana H. Ballard)
Modeling Saccadic Targeting in Visual Serach (Rajesh P.N. Rao and Gregory J. Zelinsky and Mary M. Hayhoe and Dana H. Ballard)
An Active Vision Architecture based on Iconic Representations (Rajesh P.N. Rao and Dana H. Ballard)
Learning Saccadic Eye Movements Using Multiscale Spatial Filters (Rajesh P.N. Rao and Dana H. Ballard)
Genetic Programming with Adaptive Representations (Justinian P. Rosca and Dana H. Ballard)
Seeing Behind Occlusion (Dana H. Ballard and Rajesh P.N. Rao)
The Effects of Eye Tracking in a VR Helmet on EEG Recordings (Jessica D. Bayliss and Dana H. Ballard)
Eye Movements in iconic visual search (Rajesh P.N. Rao and Gregory Zelinsky and Mary M. Hayhoe and Dana H. Ballard)
Vision in Natural and Virtual Environments (Mary M. Hayhoe and Dana H. Ballard and Jochen J. Triesch, Hiroyuki Shinoda and Pilar Aivar and Brain T. Sullivan)
A Visual Control Architecture for a Virtual Humanoid (Nathan Sprague and Dana Ballard)
Distributed Synchrony (Zuohua Zhang and Dana H. Ballard)
Localized Receptive FieldsMay Mediate Trasformation-Invariant Recognition in the Visual Cortex (Rajesh P.N. Rao and Dana H. Ballard)
Efficient Encoding of Natural Time Varying Images Produces Oriented Space-Time Receptive Fields (Rajesh P.N. Rao and Dana H. Ballard)
What you see is What you need (Jochen J. Triesch and Dana H. Ballard and Mary M. Hayhoe and Brain T. Sullivan)
Multiple-Goal Reinforcement Learning with Modular Sarsa(0) (Nathan Sprague and Dana Ballard)
A Class of Stochastic Models for Invariant Recognition (Rajesh P.N. Rao and Dana H. Ballard)
The Visual Cortext as a Hierarchical Predictor (Rajesh P.N. Rao and Dana Ballard)
Deictic Codes for the Embodiment of Cognition (Dana H. Ballard and Mary M. Hayhoe and Polly K. Pook and Rajesh P.N. Rao)
A Computational Model of Spatial Representations That Explains Object-Centered Neglect in Parietal Patients (Rajesh P.N. Rao and Dana H. Ballard)
A Model of Predictive Coding based on Spike Timing (Dana H. Ballard and Rajesh P.N. Rao and Zuohua Zhang)
Contextually Dependent Control Strategies for Manipulation (Polly K. Pook and Dana H. Ballard)
A synchronous firing model of LGN (Dana H. Ballard and Zuohua Zhang)
A Single-spike Model of Predictive Coding (Dana H. Ballard and Rajesh P.N. Rao and Zuohua Zhang)