Selection and Execution of Simple Actions via Visual Attention and Direct Parameter Specification

  • Jan Tünnermann
  • Steffen Grüne
  • Bärbel Mertsching
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10528)

Abstract

Can early visual attention processes facilitate the selection and execution of simple robotic actions? We believe that this is the case. Following the selection–for–action agenda known from human attention, we show that central perceptual processing can be avoided or at least relieved from managing simple motor processes. In an attention–classification–action cycle, salient pre-attentional structures are used to provide features to a set of classifiers. Their action proposals are coordinated, parametrized (via direct parameter specification), and executed. We evaluate the system with a simulated mobile robot.

References

  1. 1.
    Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: IEEE Proceedings of CVPR (2009)Google Scholar
  2. 2.
    Allport, D.A.: Attention and performance. In: Cognitive Psychology: New directions (1980)Google Scholar
  3. 3.
    Aziz, M.Z., Mertsching, B.: Fast and robust generation of feature maps for region-based visual attention. IEEE Trans. Image Process. 17(5), 633–644 (2008)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Aziz, M.Z., Mertsching, B.: Visual search in static and dynamic scenes using fine-grain top-down visual attention. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds.) ICVS 2008. LNCS, vol. 5008, pp. 3–12. Springer, Heidelberg (2008). doi:10.1007/978-3-540-79547-6_1 CrossRefGoogle Scholar
  5. 5.
    Balkenius, C., Hulth, N.: Attention as selection-for-action: a scheme for active perception. In: IEEE Third European Workshop on Advanced Mobile Robots (1999)Google Scholar
  6. 6.
    Borji, A., Cheng, M.M., Jiang, H., Li, J.: Salient object detection: a benchmark. IEEE Trans. Image Process. 24(12), 5706–5722 (2015)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Deubel, H., Schneider, W.X.: Saccade target selection and object recognition: evidence for a common attentional mechanism. Vis. Res. 36(12), 1827–1837 (1996)CrossRefGoogle Scholar
  8. 8.
    Fritzke, B., et al.: A growing neural gas network learns topologies. In: NIPS, vol. 7 (1995)Google Scholar
  9. 9.
    Galar, M., Fernández, A., Barrenechea, E., Bustince, H., Herrera, F.: An overview of ensemble methods for binary classifiers in multi-class problems: experimental study on one-vs-one and one-vs-all schemes. Pattern Recogn. 44(8), 1761–1776 (2011)CrossRefGoogle Scholar
  10. 10.
    Grüne, S.: Vorbereitung und Ausführung von einfachen Handlungen autonomer Roboter basierend auf raumzeitlichen Aufmerksamkeitsprozessen [Preparation and execution of simple actions in autonomous robots based on spatiotemporal attention processes]. Bachelor’s thesis, Paderborn University (2017)Google Scholar
  11. 11.
    Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. PAMI 20(11), 1254–1259 (1998)CrossRefGoogle Scholar
  12. 12.
    Koenig, N., Howard, A.: Design and use paradigms for Gazebo, an open-source multi-robot simulator. In: IEEE/RSJ Proceedings of IROS (2004)Google Scholar
  13. 13.
    Münsterberg, H.: Beiträge zur experimentellen Psychologie [Contributions to Experimental Psychology], no. 1. JCB Mohr, Heidelberg (1889)Google Scholar
  14. 14.
    Neumann, O.: Direct parameter specification and the concept of perception. Psychol. Res. 52(2–3), 207–215 (1990)CrossRefGoogle Scholar
  15. 15.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetMATHGoogle Scholar
  16. 16.
    Pratt, J., Taylor, J.E.T., Gozli, D.G.: Action and attention. In: The Handbook of Attention (2015)Google Scholar
  17. 17.
    Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y.: ROS: an open-source Robot Operating System. In: ICRA Workshop on Open Source Software (2009)Google Scholar
  18. 18.
    Rizzolatti, G., Riggio, L., Dascola, I., Umiltá, C.: Reorienting attention across the horizontal and vertical meridians: evidence in favor of a premotor theory of attention. Neuropsychologia 25(1), 31 (1987)CrossRefGoogle Scholar
  19. 19.
    Tai, L., Li, S., Liu, M.: A deep-network solution towards model-less obstacle avoidance. In: IEEE/RSJ Proceedings of IROS (2016)Google Scholar
  20. 20.
    Tünnermann, J., Born, C., Mertsching, B.: Top-down visual attention with complex templates. In: Proceedings of VISAPP, no. 1 (2013)Google Scholar
  21. 21.
    Tünnermann, J., Born, C., Mertsching, B.: Saliency from growing neural gas: learning pre-attentional structures for a flexible attention system (in preparation)Google Scholar
  22. 22.
    Tünnermann, J., Krüger, N., Mertsching, B., Mustafa, W.: Affordance estimation enhances artificial visual attention: evidence from a change-blindness study. Cogn. Comput. 7(5), 526–538 (2015)CrossRefGoogle Scholar
  23. 23.
    Tünnermann, J., Mertsching, B.: Continuous region-based processing of spatiotemporal saliency. In: Proceedings of VISAPP, no. 1 (2012)Google Scholar
  24. 24.
    Tünnermann, J., Mertsching, B.: Region-based artificial visual attention in space and time. Cogn. Comput. 6(1), 125–143 (2014)CrossRefGoogle Scholar
  25. 25.
    Wischnewski, M., Belardinelli, A., Schneider, W.X., Steil, J.J.: Where to look next? Combining static and dynamic proto-objects in a TVA-based model of visual attention. Cogn. Comput. 2(4), 326–343 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jan Tünnermann
    • 1
  • Steffen Grüne
    • 1
  • Bärbel Mertsching
    • 1
  1. 1.GET LabUniversity of PaderbornPaderbornGermany

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