Skip to main content
SpringerLink
Log in

APES: Attentively Perceiving Robot

  • Published:
Autonomous Robots Aims and scope Submit manuscript

Abstract

Robot vision systems—inspired by human-like vision—are required to employ mechanisms similar to those that have proven to be crucial in human visual performance. One of these mechanisms is attentive perception. Findings from vision science research suggest that attentive perception requires a multitude of properties: A retina with fovea-periphery distinction, an attention mechanism that can be manipulated both mechanically and internally, an extensive set of visual primitives that enable different representation modes, an integration mechanism that can infer the appropriate visual information in spite of eye, head, body and target motion, and finally memory for guiding eye movements and modeling the environment. In this paper we present an attentively “perceiving” robot called APES. The novelty of this system stems from the fact that it incorporates all of these properties simultaneously. As is explained, original approaches have to be taken to realize each of the properties so that they can be integrated together in an attentive perception framework.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Abbott, A.L. et al. 1993. Promising directions in active vision. International Journal of Computer Vision, 11(2):109–126.

    Google Scholar 

  • Albus, J.S. 1991. Outline for a theory of intelligence. IEEE Trans. Syst., Man and Cybernetics, 21(3).

  • Aloimonos, J. 1990. Purposive and qualitative active vision. In Proceedings of Image Understanding Workshop.

  • Ballard, D.H. 1991. Animate vision. Artificial Intelligence, 48:57–86.

    Article  Google Scholar 

  • Ballard, D.H. and Brown, C.M. 1992. Principles of animate vision. CVIP: Image Understanding, 56(1).

  • Ballard, D.H. 1996. On the function of visual representations. In Perception, K. Akins (ed.), Oxford University Press, pp. 111–131.

  • Bozma, H.I. and Soyer, Ç. 1994. Shape identification using probabilistic models of attentional sequences. In Proceedings of Workshop on Machine Vision Applications, IAPR.

  • Chown, E., Kaplan, S., and Kortenkamp, D. 1995. Prototypes, location, and associative networks (PLAN): Towards a unified theory of cognitive mapping. Cognitive Science, 19:1–51.

    Article  Google Scholar 

  • Clark, J. 1999. Spatial attention and latencies in saccadic eye movements. Vision Research, 39:585–602.

    Article  Google Scholar 

  • Fiala, J.C. et al. 1994. TRICLOPS: A tool for studying active vision. International Journal of Computer Vision, 12(2/3):231–250.

    Google Scholar 

  • Gallant, J.L., Connor, C.E., Rakshit, S., Lewis, J.W., and Van Essen, D.C. 1996. Neural responses to polar, hyperbolic and cartesian gratings in area V4 of the Macaque monkey. Journal of Neurophysiology, 76(4):2718–2739.

    Google Scholar 

  • Gouras, P. 1986. Oculomotor system. In Principles of Neural Science, J.H. Schwartz and E.R. Kandel (eds.), Elsevier.

  • Gouras, P. and Bailey, C.H. 1986. The retina and phototransduction. In Principles of Neural Science, J.H. Schwartz and E.R. Kandel (eds.), Elsevier.

  • Greene, H.H. 1999. Temporal relationships between eye fixations and manual reactions in visual search. Acta Psychologica, 101:105–123.

    Article  Google Scholar 

  • Grosso, E., Manzotti, E., Tiso, R., and Sandini, G. 1995. A space-variant approach to oculomotor control. In Proceedings of International Symposium on Computer Vision, pp. 509–514.

  • Hubel, D.H. 1988. Eye, brain and vision. Scientific American Lib.

  • Huber, E. and Kortenkamp, D. 1998. A behavior-based approach to active stereo vision for mobile robots. Artificial Intelligence, 11:229–243.

    Google Scholar 

  • Itti, L. and Koch, C. 2001. Computational modeling of visual attention. Nature Reviews, 2.

  • Julesz, B. 1995. Dialogues on Perception. MIT Book Press, Cambridge, MA.

  • Keskinpala, T. et al. 2003. Knowledge-sharing techniques for egocentric navigation, IEEE Conference on Systems, Man and Cybernetics, pp. 2469–2476.

  • Kowler, E. 1995. Eye movements. In Visual Cognition, S.M. Kosslyn, D.N. Osherson (eds.), MIT Press, pp. 215–266.

  • Lago-Fernandez, L.F., Sanchez-Montanes, M.A., and Cobacho, F. 2001. A biologically inspired visual system for an autonomous robot. Neurocomputing, 38–40:1385–1391.

    Google Scholar 

  • McGaugh, J.L., Weinberger, N.M., and Lynch, G. (eds.) 1995. Brain and Memory. Oxford University Press.

  • Noton, D. and Stark, L. 1971. Scan paths in eye movements during pattern recognition. Science, 171:308–311.

    Google Scholar 

  • Palmer, J., Verghese, P., and Pavel, M. 2000. The psychophysics of visual search. Vision Research, 40:1227–1268.

    Article  Google Scholar 

  • Papanikolopoulos, N.P. 1994. Adaptive control, visual servoing, and controlled active vision. In Proceedings of IEEE International Conference on Robotics and Automation.

  • Peters, R.A. et al. 2001. The sensory ego-sphere as a short-term memory for humanoids. In Proceedings of the IEEE-RAS International Conference on Humanoid Robots.

  • Rao, R.P.N. et al. 1996. Modeling saccadic tergeting in visual search. In Advances in Neural Information Processing Systems 8 (NIPS*95), D. Touretzky, M. Mozer, and M. Hasselmo (eds.), MIT Press.

  • Rimey, R.D. and Brown, C.M. 1990. Selective attention as sequential behaviour: Modelling eye movements with an augmented hidden Markov model. Technical Report, The University of Rochester, Computer Science Department.

  • Rimey, R.D. and Brown, C. 1994. Control of selective perception using bayes nets and decision theory. International Journal of Computer Vision, 12(2/3):173–207.

    Google Scholar 

  • Rybak, I.A., Gusakova, V.I., Golovan, A.V., Podladchikova, L.N., and Shevtsova, N.A. 1998. A model of attention-guided visual perception and recognition. Vision Research, Special Issue: Models of Recognition.

  • Schlingensiepen, K.H. et al. 1986. The importance of eye movements in the analysis of simple patterns. Vision Research, 26(7):1111–1117.

    Article  Google Scholar 

  • Shafer, G. 1976. A Mathematical Theory of Evidence. Princeton University Press.

  • Shin, C.W., Inokuchi, S., and Kim, K.I. 1997. Retina-like visual sensor for fast tracking and navigation robots. Machine Vision and Applications, 10:1–8.

    Article  Google Scholar 

  • Soyer, Ç. and Bozma, H.I. 1997. Further experiments in classification of attentional sequences: Combining instantaneous and temporal evidence. In Proceedings of IEEE 8th International Conference on Advanced Robotics, ICAR.

  • Soyer, Ç., Bozma, H.I., and Istefanopulos, Y. 2000. A new memory model for selective perception systems. In Proceedings of IEEE RSC International Conference on Intelligent Robots and Systems, IROS.

  • Soyer, Ç. and Bozma, H.I. 2003. Attentional sequence based recognition: Markovian and Evidential Reasoning. IEEE Transactions on Systems, Man and Cybernetics, 33(6):937–950.

    Google Scholar 

  • Soyer, Ç. 2002. A model of active and attentive vision. PhD dissertation. Bogazici University.

  • Stark, L. and Ellis, S.R. 1981. Scan paths revisited: Cognitive models direct active looking. In Eye Movements: Cognition and Visual Perception, Fisher, Monty and Senders (eds.), Erlbaum, NJ, pp. 193–226.

  • Tagare, H., Toyama, K., and Wang, J.G. 2001. A maximum likelihood strategy for directing attention during visual search. IEEE Transactions on PAMI, 23(5):491–500.

    Google Scholar 

  • Treisman, A. and Gelade, G. 1980. A feature integration theory of attention. Cognitive Psychology, 12:97–136.

    Article  Google Scholar 

  • Tsotsos, J.K. et al. 1995 Modeling visual attention via selective tuning. Artificial Intelligence, 78:507–545.

    Article  MathSciNet  Google Scholar 

  • Viviani, P. 1990. Eye movements in visual search: Cognitive, perceptual and motor control aspects. In Eye Movements and Their Role in Visual and Cognitive Processes, Elsevier, pp. 71–112.

  • Wasson, G., Kortenkamp, D., and Huber, E. 1999. Integrating active perception with an autonomous robot architecture. Robotics and Autonomous Systems, 29:175–180.

    Article  Google Scholar 

  • Westin, C. et al. 1996. Attention control for robot vision. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR, p. 726.

  • Yeap, W.K. 1988. Toward of a computational theory of cognitive maps. Artificial Intelligence, 34:297–360.

    Article  Google Scholar 

  • Yeap, W.K. and Jefferies, M.E. 1999. Computing a representation of the local environment. Artificial Intelligence, 107:265–301.

    Article  Google Scholar 

  • Zeki, S. 1992. The visual image in mind and brain. Scientific American, 267(3).

  • Zingale, C.M. and Kowler, E. 1987. Planning sequences of saccades. Vision Research, 27(8):1327–1341.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Biomedical Engineering, Boğaziçi University, 80015 Bebek, İstanbul, Turkey

    Ç Soyer & Y. İstefanopulos

  2. Intelligent Systems Laboratory, Department of Electrical Electronic Engineering, Boğaziçi University, 80015 Bebek, Istanbul, Turkey

    Ç Soyer, H. I. Bozma & Y. İstefanopulos

Authors
  1. Ç Soyer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. H. I. Bozma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Y. İstefanopulos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ç Soyer.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Soyer, Ç., Bozma, H.I. & İstefanopulos, Y. APES: Attentively Perceiving Robot. Auton Robot 20, 61–80 (2006). https://doi.org/10.1007/s10514-006-5538-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10514-006-5538-0

Keywords

Search

Navigation