Dana Harry Ballard (1946-2022) was a professor of

computer science Computer science is the study of computation, automation, and information. Computer science spans theoretical disciplines (such as algorithms, theory of computation, information theory, and automation) to Applied science, practical discipli ...

at the

University of Texas at Austin The University of Texas at Austin (UT Austin, UT, or Texas) is a public research university in Austin, Texas. It was founded in 1883 and is the oldest institution in the University of Texas System. With 40,916 undergraduate students, 11,075 ...

and formerly with the

University of Rochester The University of Rochester (U of R, UR, or U of Rochester) is a private research university in Rochester, New York. The university grants undergraduate and graduate degrees, including doctoral and professional degrees. The University of Roc ...

.Personal Website
/ref> Ballard attended

MIT The Massachusetts Institute of Technology (MIT) is a private land-grant research university in Cambridge, Massachusetts. Established in 1861, MIT has played a key role in the development of modern technology and science, and is one of the m ...

and graduated in 1967 with his bachelor's degree in aeronautics and astronautics. He then attended the University of Michigan for his masters in information and control engineering in 1970. He received his Ph.D. from the

University of California, Irvine The University of California, Irvine (UCI or UC Irvine) is a public land-grant research university in Irvine, California. One of the ten campuses of the University of California system, UCI offers 87 undergraduate degrees and 129 graduate and pr ...

in information engineering in 1974. He did research in

artificial intelligence Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by animals and humans. Example tasks in which this is done include speech re ...

and human cognition and perception with a focus on the human visual system. In 1982, with Christopher M. Brown he authored a pioneering textbook in the field of

computer vision Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the hum ...

, titled Computer Vision. He also popularized the use of the

generalised hough transform The generalized Hough transform (GHT), introduced by Dana H. Ballard in 1981, is the modification of the Hough transform using the principle of template matching. The Hough transform was initially developed to detect analytically defined shapes ( ...

in computer vision in his paper "Generalizing the Hough Transform to Detect Arbitrary Shapes." He is also known as a proponent of

active vision An area of computer vision is active vision, sometimes also called active computer vision. An active vision system is one that can manipulate the viewpoint of the camera(s) in order to investigate the environment and get better information from it. ...

techniques for

systems as well as approaches to understanding human vision.Ballard, D. H. and Hayhoe, M. M.(2009) Modeling the role of task in the control of gaze, Visual Cognition, 17, 1185-1204 His textbook titled "An Introduction to Natural Computation" (1997) combines introductory material on varied subjects relevant to computing in the brain, such as

neural networks A neural network is a network or circuit of biological neurons, or, in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus, a neural network is either a biological neural network, made up of biological ...

reinforcement learning Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine ...

, and genetic learning. His most recent book, "Brain Computation as Hierarchical Abstraction," describes a multilevel approach to understanding neural computation.

References

Further reading