Abstract
The aim of this paper is to present our attempt in creating a visual system for a humanoid robot, which can intervene in non-specific tasks in real-time. Due to the generic aspects of our goal, our models are based around human architecture. Such approaches have usually been contradictory, with the efficient implementation of real systems and its demanding computational cost. We show that by using PredN1, a system for developing distributed real-time robotic applications, we are able to build a real-time scalable visual attention system. It is easy to change the structure of the system, or the hardware in order to investigate new models. In our presentation, we will also present our system with a number of human visual attributes, such as: log-polar retino-cortical mapping, banks of oriented filters providing a generic signature of any object in an image. Additionally, a visual attention mechanism — a psychophysical model — FeatureGate, is used in eliciting a fixation point. The system runs at frame rate, allowing interaction of same time scale as humans.
Parallel Real time Event and Data driven Network
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Ruggero Milanese, Detecting salient regions in an image: from biological evidence to computer implementation, Ph.D. thesis, Departement of Computer Science, University of Geneva, Switzerland, 1993.
Colby, “The neuroanatomy and neurophysiology of attention,” Journal of Child Neurology, vol. 6, pp. 90–118, 1991.
Westelius C.J., Focus of Attention and Gaze Control for Robot Vision, Ph.D. thesis, Linkoping University, 1995.
Atsuto Maki, Stereo Vision in Attentive Scene Analysis, Ph.D. thesis, KTH Computational Vision and Active Perception Laboratory (CVAP), 1996.
Rajesh P.N. Rao and Dana H. Ballard, “An active vision architecture based on iconic representations,” Artificial Intelligence Journal, vol. 78, pp. 461–505, 1995.
William T. Freeman, “The design and use of steerable filters,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13(9), pp. 891–906, September 1991.
Gal Sela and Martin D. Levine, “Real-time attention for robotic vision,” Real-time Imaging, vol. 3, pp. 173–194, 1997.
Barbel Mertsching and Maik Bollmann, “Visual attention and gaze control for an active vision system,” Progress in connectionnist-based information systems. Springer, vol. 1, pp. 76–79, 1997.
C. Capuro, F. Panerai, and G. Sandini, “Dynamic vergence using log-polar images,” International Journal of Computer Vision, vol. 24, pp. 79–94, 1997.
Marc Bolduc and Martin D. Levine, “A review of biologically motivated space-variant data reduction models for robotic vision,” Computer Vision and image understanding, vol. 69, pp. 170–184, 1998.
C. Braccini, G. Gambarella, G. Sandini, and V. Tagliasco, “A model of the early stages of the human visual system: Functional and topological transformations performed in the peripheral visual field,” Biological Cybernetics, vol. 44, pp. 47–58, 1982.
E. L. Schwartz, “Computational anatomy and functional architecture of the striate cortex,” Vision Research, vol. 20, pp. 645–669, 1980.
Rajesh P. N. Rao and Dana H. Ballard, “Efficient encoding of natural time varying images produces oriented space-time receptive fields (technical report),” Tech. Rep., National Resource Laboratory for the Study of Brain and Behavior, Department of Computer Science, University of Rochester, August 1997.
K. R. Cave, “The featuregate model of visual selection,” (in review), 1998.
J. A. Driscoll, R. A. Peters II, and K. R. Cave, “A visual attention network for a humanoid robot,” in IROS98, 1998.
Jeremy M. Wolfe, “Visual search: A review,” Visual Search, Attention, 1996.
Zhihua Wu and Aike Guo, “Selective visual attention in a neurocomputational model of phase oscillators,” Biological cybernetics, vol. 80, pp. 205–214, 1999.
Olivier Stasse and Yasuo Kuniyoshi, “Predn: Achieving efficiency and code reusability in a programming system for complex robotic applications,” Submitted to the International Conference on Robotics and Automation, 2000.
S. Rougeaux and Y. Kuniyoshi, “Robust real-time tracking on an active vision head,” in Proc. Int. Conf. on Intelligent Robots and Systems (IROS’97), September 1997, 7–11.
J. Dias Arajo, C. Paredes, and J. Batista, “Optical normal flow estimation on log-polar images. a solution for real-time binocular vision,” Real-Time Imaging, vol. 3(3), pp. 213–228, June 1997.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Stasse, O., Kuniyoshi, Y., Cheng, G. (2000). Development of a Biologically Inspired Real-Time Visual Attention System. In: Lee, SW., Bülthoff, H.H., Poggio, T. (eds) Biologically Motivated Computer Vision. BMCV 2000. Lecture Notes in Computer Science, vol 1811. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45482-9_15
Download citation
DOI: https://doi.org/10.1007/3-540-45482-9_15
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67560-0
Online ISBN: 978-3-540-45482-3
eBook Packages: Springer Book Archive