Skip to main content
SpringerLink
Log in

Active Sensorimotor Object Recognition in Three-Dimensional Space

  • Conference paper
Spatial Cognition IX (Spatial Cognition 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8684))

Included in the following conference series:

Abstract

Spatial interaction of biological agents with their environment is based on the cognitive processing of sensory as well as motor information. There are many models for sole sensory processing but only a few for integrating sensory and motor information into a unifying sensorimotor approach. Additionally, neither the relations shaping the integration are yet clear nor how the integrated information can be used in an underlying representation. Therefore, we propose a probabilistic model for integrated processing of sensory and motor information by combining bottom-up feature extraction and top-down action selection embedded in a Bayesian inference approach. The integration of sensory perceptions and motor information brings about two main advantages: (i) Their statistical dependencies can be exploited by representing the spatial relationships of the sensor information in the underlying joint probability distribution and (ii) a top-down process can compute the next most informative region according to an information gain strategy. We evaluated our system in two different object recognition tasks. We found that the integration of sensory and motor information significantly improves active object recognition, in particular when these movements have been chosen by an information gain strategy.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Gibson, J.: The ecological approach to visual perception. Houghton Mifflin, Boston (1992)

    Google Scholar 

  2. Neisser, U.: Cognition and reality: Principles and implications of cognitive psychology. WH Freeman/Times Books/Henry Holt & Co. (1976)

    Google Scholar 

  3. Bajcsy, R.: Active perception. Proceedings of the IEEE 76(8), 966–1005 (1988)

    Article  Google Scholar 

  4. Aloimonos, J., Weiss, I., Bandyopadhyay, A.: Active vision. International Journal of Computer Vision 1(4), 333–356 (1988)

    Article  Google Scholar 

  5. Ballard, D.H.: Animate vision. Artificial intelligence 48(1), 57–86 (1991)

    Article  Google Scholar 

  6. O’Regan, J.K., Noë, A.: A sensorimotor account of vision and visual consciousness. Behavioral and Brain Sciences 24(5), 939–972 (2001)

    Article  Google Scholar 

  7. Noë, A.: Action in Perception. MIT Press (2004)

    Google Scholar 

  8. Prinz, W.: A common coding approach to perception and action. Springer (1990)

    Google Scholar 

  9. Hommel, B., Müsseler, J., Aschersleben, G., Prinz, W.: The theory of event coding (TEC): A framework for perception and action planning. Behavioral and Brain Sciences 24(05), 849–878 (2001)

    Article  Google Scholar 

  10. O’Regan, J.K.: What it is like to see: A sensorimotor theory of perceptual experience. Synthese 129(1), 79–103 (2001)

    Article  Google Scholar 

  11. Noton, D., Stark, L.: Scanpaths in saccadic eye movements while viewing and recognizing patterns. Vision Research 11(9), 929–IN8 (1971)

    Google Scholar 

  12. Stark, L.W., Choi, Y.S.: Experimental metaphysics: The scanpath as an epistemological mechanism. In: Zangemeister, W.H., Stiehl, H.S., Freksa, C. (eds.) Visual Attention and Cognition. Advances in Psychology, vol. 116, pp. 3–69. North-Holland (1996)

    Google Scholar 

  13. Quinlan, J.R.: Induction of decision trees. Machine Learning 1(1), 81–106 (1986)

    Google Scholar 

  14. Cassandra, A.R., Kaelbling, L.P., Kurien, J.A.: Acting under uncertainty: Discrete Bayesian models for mobile-robot navigation. In: Proceedings of the 1996 IEEE/RSJ International Conference on Intelligent Robots and Systems IROS 1996, vol. 2, pp. 963–972. IEEE (1996)

    Google Scholar 

  15. Stachniss, C., Grisetti, G., Burgard, W.: Information Gain-based Exploration Using Rao-Blackwellized Particle Filters. In: Robotics: Science and Systems, vol. 2, pp. 65–72 (2005)

    Google Scholar 

  16. Oaksford, M., Chater, N.: Information gain explains relevance which explains the selection task. Cognition 57(1), 97–108 (1995)

    Article  Google Scholar 

  17. Friston, K., Kilner, J., Harrison, L.: A free energy principle for the brain. Journal of Physiology-Paris 100(1-3), 70–87 (2006); heoretical and Computational Neuroscience: Understanding Brain Functions

    Google Scholar 

  18. Schill, K., Pöppel, E., Zetzsche, C.: Completing knowledge by competing hierarchies. In: Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence, pp. 348–352. Morgan Kaufmann Publishers Inc. (1991)

    Google Scholar 

  19. Schill, K.: Decision support systems with adaptive reasoning strategies. In: Freksa, C., Jantzen, M., Valk, R. (eds.) Foundations of Computer Science. LNCS, vol. 1337, pp. 417–427. Springer, Heidelberg (1997)

    Chapter  Google Scholar 

  20. Zetzsche, C., Schill, K., Deubel, H., Krieger, G., Umkehrer, E., Beinlich, S.: Investigation of a sensorimotor system for saccadic scene analysis: an integrated approach. In: Proceedings of the 5th International Conference of Simulation of Adaptive Behaviour, vol. 5, pp. 120–126 (1998)

    Google Scholar 

  21. Schill, K., Umkehrer, E., Beinlich, S., Krieger, G., Zetzsche, C.: Scene analysis with saccadic eye movements: top-down and bottom-up modeling. Journal of Electronic Imaging 10(1), 152–160 (2001)

    Article  Google Scholar 

  22. Zetzsche, C., Wolter, J., Schill, K.: Sensorimotor representation and knowledge-based reasoning for spatial exploration and localisation. Cognitive Processing 9(4), 283–297 (2008)

    Article  Google Scholar 

  23. Reineking, T., Wolter, J., Gadzicki, K., Zetzsche, C.: Bio-inspired Architecture for Active Sensorimotor Localization. In: Hölscher, C., Shipley, T.F., Olivetti Belardinelli, M., Bateman, J.A., Newcombe, N.S. (eds.) Spatial Cognition VII. LNCS, vol. 6222, pp. 163–178. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  24. Schill, K., Zetzsche, C., Hois, J.: A belief-based architecture for scene analysis: From sensorimotor features to knowledge and ontology. Fuzzy Sets and Systems 160(10), 1507–1516 (2009)

    Article  MathSciNet  Google Scholar 

  25. Griffin, G., Holub, A., Perona, P.: Caltech-256 Object Category Dataset. Technical report, California Institute of Technology (2007)

    Google Scholar 

  26. Oliva, A., Torralba, A.: Building the gist of a scene: The role of global image features in recognition. Prorgess in Brain Research 155, 23–36 (2006)

    Article  Google Scholar 

  27. Oliva, A., Torralba, A.: Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision 42(3), 145–175 (2001)

    Article  MATH  Google Scholar 

  28. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Computer Vision and Image Understanding 110(3), 346–359 (2008)

    Article  Google Scholar 

  29. Roy, N., Burgard, W., Fox, D., Thrun, S.: Coastal navigation-mobile robot navigation with uncertainty in dynamic environments. In: Proceedings of the 1999 IEEE International Conference on Robotics and Automation, vol. 1, pp. 35–40. IEEE (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Cognitive Neuroinformatics, University of Bremen, Enrique-Schmidt-Straße 5, 28359, Bremen, Germany

    David Nakath, Tobias Kluth, Thomas Reineking, Christoph Zetzsche & Kerstin Schill

Authors
  1. David Nakath
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tobias Kluth
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thomas Reineking
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Christoph Zetzsche
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kerstin Schill
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Universität Bremen, Enrique-Schmidt-Str. 5, 28359, Bremen, Germany

    Christian Freksa  & Thomas Barkowsky  & 

  2. Department of Computer Science, Universität Freiburg, Georges-Köhler-Allee 52, 79110, Freiburg, Germany

    Bernhard Nebel

  3. University of California, 93106, Santa Barbara, CA, USA

    Mary Hegarty

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Nakath, D., Kluth, T., Reineking, T., Zetzsche, C., Schill, K. (2014). Active Sensorimotor Object Recognition in Three-Dimensional Space. In: Freksa, C., Nebel, B., Hegarty, M., Barkowsky, T. (eds) Spatial Cognition IX. Spatial Cognition 2014. Lecture Notes in Computer Science(), vol 8684. Springer, Cham. https://doi.org/10.1007/978-3-319-11215-2_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-11215-2_22

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11214-5

  • Online ISBN: 978-3-319-11215-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation