In this paper we describe the architecture and data structure of space-variant sensors with reconfigurable cartesian geometries. The ability of these sensors to change the position and size of their high resolution regions or electronic foveas, makes them suitable to compensate the limited performance or coarse fixation characteristics of the mechanical systems utilized for gaze tasks in active vision applications where size, weight or cost could be conditioning factors to the performance or feasibility of the whole system. An alternative to the implementation of these sensors is based on off-the-shelf CCD cameras and devices with reconfiguration capabilities, such as FPGAs. In this way, besides the multiresolution data output, sensor reconfiguration systems let generate additional data adapted to the functions of the higher level modules of the active vision systems. As a result of this computing capability at the sensor level, it is possible to unload the processing stages of certain tasks without penalty in time or significant addition of hardware. An approach to selective foveation tasks and motion detection is presented.
[1]
Francisco Sandoval Hernández,et al.
Shifted fovea multiresolution geometries
,
1996,
Proceedings of 3rd IEEE International Conference on Image Processing.
[2]
Giulio Sandini,et al.
Estimation of depth from motion using an anthropomorphic visual sensor
,
1990,
Image Vis. Comput..
[3]
Giorgio Bonmassar,et al.
Space-variant active vision: Definition, overview and examples
,
1995,
Neural Networks.
[4]
Cesar Bandera,et al.
Foveal machine vision systems
,
1989,
Conference Proceedings., IEEE International Conference on Systems, Man and Cybernetics.
[5]
Giulio Sandini,et al.
A Foveated Retina-Like Sensor Using CCD Technology
,
1989,
Analog VLSI Implementation of Neural Systems.
[6]
Eric L. Schwartz,et al.
Design considerations for a space-variant visual sensor with complex-logarithmic geometry
,
1990,
[1990] Proceedings. 10th International Conference on Pattern Recognition.