Active Vision explores important themes emerging from the active
vision paradigm, which has only recently become an established area
of machine vision. In four parts the contributions look in turn at
tracking, control of vision heads, geometric and task planning, and
architectures and applications, presenting research that marks a
turning point for both the tasks and the processes of computer
vision.The eighteen chapters in Active Vision draw on traditional
work in computer vision over the last two decades, particularly in
the use of concepts of geometrical modeling and optical flow;
however, they also concentrate on relatively new areas such as
control theory, recursive statistical filtering, and dynamical
modeling.Active Vision documents a change in emphasis, one that is
based on the premise that an observer (human or computer) may be
able to understand a visual environment more effectively and
efficiently if the sensor interacts with that environment, moving
through and around it, culling information selectively, and
analyzing visual sensory data purposefully in order to answer
specific queries posed by the observer. This method is in marked
contrast to the more conventional, passive approach to computer
vision where the camera is supposed to take in the whole scene,
attempting to make sense of all that it sees.Andrew Blake is
Lecturer in Engineering Science at the University of Oxford Alan
Yuille is Associate Professor in the Division of Applied Sciences
at Harvard University.
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