Matches in DBpedia 2014 for { ?s ?p Dana Harry Ballard (born 1946) is a professor of computer science currently at the University of Texas at Austin and formerly with the University of Rochester.Ballard received his Ph.D. from the University of California, Irvine. He has done research in artificial intelligence 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, titled Computer Vision. He also popularized the use of the generalised hough transform in computer vision in his paper "Generalizing the Hough Transform to Detect Arbitrary Shapes." He is also known as a proponent of active vision techniques for computer vision systems as well as approaches to understanding human vision. He has written a textbook titled "An Introduction to Natural Computation" (1997). It combines introductory material on varied subjects relevant to computing in the brain, such as neural networks, reinforcement learning, and genetic learning.. }
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- Dana_H._Ballard abstract "Dana Harry Ballard (born 1946) is a professor of computer science currently at the University of Texas at Austin and formerly with the University of Rochester.Ballard received his Ph.D. from the University of California, Irvine. He has done research in artificial intelligence 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, titled Computer Vision. He also popularized the use of the generalised hough transform in computer vision in his paper "Generalizing the Hough Transform to Detect Arbitrary Shapes." He is also known as a proponent of active vision techniques for computer vision systems as well as approaches to understanding human vision. He has written a textbook titled "An Introduction to Natural Computation" (1997). It combines introductory material on varied subjects relevant to computing in the brain, such as neural networks, reinforcement learning, and genetic learning.".