We present a binocular active vision system that can attend to and fixate a moving target. Our system has an open and expandable design and it forms the first steps of a long term effort towards developing an active observer using vision to interact with the environment, in particular capable of figure-ground segmentation. We also present partial real-time implementations of this system and show their performance in real-world situations together with motor control. In pursuit we particularly focus on occlusions of other targets, both stationary and moving, and integrate three cues, ego-motion, target motion and target disparity, to obtain an overall robust behavior. An active vision system must be open, expandable, and operate with whatever data are available momentarily. It must also be equipped with means and methods to direct and change its attention. This system is therefore equipped with motion detection for changing attention and pursuit for maintaining attention, both of which run concurrently.>

It is well-known that active selection of fixation points in humans is highly context and task dependent. It is therefore likely that successful computational processes for fixation in active vision should be so too. We are considering active fixation in the context of recognition of man-made objects characterized by their shapes. In this situation the qualitative shape and type of observed junctions play an important role. The fixations are driven by a grouping strategy, which forms sets of connected junctions separated from the surrounding at depth discontinuities. We have furthermore developed a methodology for rapid active detection and classification of junctions by selection of fixation points. The approach is based on direct computations from image data and allows integration of stereo and accommodation cues with luminance information. This work form a part of an effort to perform active recognition of generic objects, in the spirit of Malik and Biederman, but on real imagery rather than on line-drawings.

Fixation is the link between the observer and the events in the outside world and is a key element in the control strategy of an active vision system. The authors describe how the fixation process functions in human beings in general and discuss one of the essential components of fixation, vergence, in particular. They discuss the components of the human fixation model and through its geometry and organization describe a machine fixation model, running in real-time on a head-eye system. The authors are motivated by the need for gaze control in head-eye systems, which entails the need to clarify some fundamental relations in primate vergence movements, namely, the relations between disparity and blur and between vergence and accommodation. Following the geometry behind vergence movements, it is also suggested that the mechanism for disparity detection can use the symmetry present in the pattern of the left and right images through isolating vergence from version.>