Abstract
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.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
J.Van der Spiegel, G.Kreider, C.Claeis, I.Debusschere, G.Sandini, F. Fantini, G.Soncini, “A foveated retina-like sensor using CCD technology” in Analog VLSI implementation of Neural Systems, C.Mead, M.Ismail, Eds.,KluwerAcad. Publ, 1989.
M.Tistarelli, G.Sandini,“Estimation of depth from motion using anthropomorphic visual sensor”, Image and Vision Computing, vol.8, No. 4, pp. 271–278, 1990.
A.S.Rojer, E.L.Schwartz,“Design considerations for a space-variant visual sensor with complex logarithmic geometry”, in Proceedings of the 10th. International Conference on Pattern Recognition, pp.278–285, 1990.
C.Bandera, P. Scott,.: “Foveal Machine Vision Systems” in IEEE International Conf. on Systs., Man and Cybernetics, Cambridge, MA, pp.596–599. November 1989.
E. Schwartz, D.N Greve, G.Bonmassar, “Space-variant Active Vision: Definition, Overview and Examples”. Neural Networks, Vo1.8, No. 7/8, pp. 1297–1308, 1995.
P.Camacho, F.Arrebola, F.Sandoval, “Shifted Fovea Multiresolution Geometries” in Proceedings of the IEEE International Conference on Image Processing, Vol.I pp.307–310. Lausanne, Switzerland. 1996.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Camacho, P., Arrebola, F., Sandoval, F. (1997). Adaptive fovea structures for space-variant sensors. In: Del Bimbo, A. (eds) Image Analysis and Processing. ICIAP 1997. Lecture Notes in Computer Science, vol 1310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63507-6_228
Download citation
DOI: https://doi.org/10.1007/3-540-63507-6_228
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63507-9
Online ISBN: 978-3-540-69585-1
eBook Packages: Springer Book Archive