Abstract
3D shape determines an object’s physical properties to a large degree. In this article, we introduce an autonomous learning system for categorizing 3D shape of simulated objects from single views. The system extends an unsupervised bottom-up learning architecture based on the slowness principle with top-down information derived from the physical behavior of objects. The unsupervised bottom-up learning leads to pose invariant representations. Shape specificity is then integrated as top-down information from the movement trajectories of the objects. As a result, the system can categorize 3D object shape from a single static object view without supervised postprocessing.
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References
Einhäuser, W., Hipp, J., Eggert, J., Körner, E., König, P.: Learning viewpoint invariant object representations using a temporal coherence principle. Biol. Cyber. 93, 79–90 (2005)
Franzius, M., Sprekeler, H., Wiskott, L.: Slowness and sparseness lead to place, head-diretion and spatial-view cells. PLoS Comp. Biol. 3(8), e166 (2007)
Franzius, M., Wilbert, N., Wiskott, L.: Invariant object recognition with slow feature analysis. In: Kůrková, V., Neruda, R., Koutník, J. (eds.) ICANN 2008, Part I. LNCS, vol. 5163, pp. 961–970. Springer, Heidelberg (2008)
Gupta, A., van der Meer, M., Touretzky, D., Redish, A.: Hippocampal replay is not a simple function of experience. Neuron 65(5), 695–705 (2010)
Metta, G., Fitzpatrick, P.: Better vision through manipulation. In: Proc. 2nd Inter. Workshop on Epigenetic Robotics, vol. 11, pp. 109–128 (2002)
Ridge, B., Skočaj, D., Leonardis, A.: A system for learning basic object affordances using a self-organizing map. In: Proc. ICCS (2008)
Rolls, E.T., Stringer, S.M.: Invariant visual object recognition: A model, with lighting invariance. Journal of Physiology - Paris 100, 43–62 (2006)
Stark, M., Lies, P., Zillich, M., Wyatt, J., Schiele, B.: Functional object class detection based on learned affordance cues. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds.) ICVS 2008. LNCS, vol. 5008, pp. 435–444. Springer, Heidelberg (2008)
Wiskott, L., Sejnowski, T.: Slow feature analysis: Unsupervised learning of invariances. Neural Comp. 14(4), 715–770 (2002)
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Franzius, M., Wersing, H. (2010). Learning Invariant Visual Shape Representations from Physics. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6354. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15825-4_38
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DOI: https://doi.org/10.1007/978-3-642-15825-4_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15824-7
Online ISBN: 978-3-642-15825-4
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