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.
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References
Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: IEEE Proceedings of CVPR (2009)
Allport, D.A.: Attention and performance. In: Cognitive Psychology: New directions (1980)
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)
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
Balkenius, C., Hulth, N.: Attention as selection-for-action: a scheme for active perception. In: IEEE Third European Workshop on Advanced Mobile Robots (1999)
Borji, A., Cheng, M.M., Jiang, H., Li, J.: Salient object detection: a benchmark. IEEE Trans. Image Process. 24(12), 5706–5722 (2015)
Deubel, H., Schneider, W.X.: Saccade target selection and object recognition: evidence for a common attentional mechanism. Vis. Res. 36(12), 1827–1837 (1996)
Fritzke, B., et al.: A growing neural gas network learns topologies. In: NIPS, vol. 7 (1995)
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)
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)
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)
Koenig, N., Howard, A.: Design and use paradigms for Gazebo, an open-source multi-robot simulator. In: IEEE/RSJ Proceedings of IROS (2004)
Münsterberg, H.: Beiträge zur experimentellen Psychologie [Contributions to Experimental Psychology], no. 1. JCB Mohr, Heidelberg (1889)
Neumann, O.: Direct parameter specification and the concept of perception. Psychol. Res. 52(2–3), 207–215 (1990)
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)
Pratt, J., Taylor, J.E.T., Gozli, D.G.: Action and attention. In: The Handbook of Attention (2015)
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)
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)
Tai, L., Li, S., Liu, M.: A deep-network solution towards model-less obstacle avoidance. In: IEEE/RSJ Proceedings of IROS (2016)
Tünnermann, J., Born, C., Mertsching, B.: Top-down visual attention with complex templates. In: Proceedings of VISAPP, no. 1 (2013)
Tünnermann, J., Born, C., Mertsching, B.: Saliency from growing neural gas: learning pre-attentional structures for a flexible attention system (in preparation)
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)
Tünnermann, J., Mertsching, B.: Continuous region-based processing of spatiotemporal saliency. In: Proceedings of VISAPP, no. 1 (2012)
Tünnermann, J., Mertsching, B.: Region-based artificial visual attention in space and time. Cogn. Comput. 6(1), 125–143 (2014)
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)
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Tünnermann, J., Grüne, S., Mertsching, B. (2017). Selection and Execution of Simple Actions via Visual Attention and Direct Parameter Specification. In: Liu, M., Chen, H., Vincze, M. (eds) Computer Vision Systems. ICVS 2017. Lecture Notes in Computer Science(), vol 10528. Springer, Cham. https://doi.org/10.1007/978-3-319-68345-4_36
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DOI: https://doi.org/10.1007/978-3-319-68345-4_36
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