Abstract
The combination of vision and speech, together with the resulting necessity for formal representations, builds a central component of an autonomous system. A robot that is supposed to navigate autonomously through space must be able to perceive its environment as automatically as possible. But each recognition system has its own inherent limits. Especially a robot whose task is to navigate through unknown terrain has to deal with unidentified or even unknown objects, thus compounding the recognition problem still further. The system described in this paper takes this into account by trying to identify objects based on their functionality where possible. To handle cases where recognition is insufficient, we examine here two further strategies: on the one hand, the linguistic reference and labeling of the unidentified objects and, on the other hand, ontological deduction. This approach then connects the probabilistic area of object recognition with the logical area of formal reasoning. In order to support formal reasoning, additional relational scene information has to be supplied by the recognition system. Moreover, for a sound ontological basis for these reasoning tasks, it is necessary to define a domain ontology that provides for the representation of real-world objects and their corresponding spatial relations in linguistic and physical respects. Physical spatial relations and objects are measured by the visual system, whereas linguistic spatial relations and objects are required for interactions with a user.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Anguelov, D., Taskar, B., Chatalbashev, V., Koller, D., Gupta, D., Heitz, G., Andrew Y. N.: Discriminative learning of markov random fields for segmentation of 3D range data. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, California (June 2005)
Bateman, J.A., Farrar, S.: Spatial Ontology Baseline. SFB/TR8 internal report I1-[OntoSpace] D2, Collaborative Research Center for Spatial Cognition, University of Bremen, University of Freiburg, Germany (2004)
Berners-Lee, T., Hendler, J., Lassila, O.: The Semantic Web. Scientific American 284(5), 34–43 (2001)
Anthony, G., Cohn, A.G., Bennett, B., Gooday, J., Gotts, N.M.: Qualitative spatial representation and reasoning with the region connection calculus. GeoInformatica 1(3), 275–316 (1997)
Coventry, K.R., Carmichael, R., Garrod, S.C.: Spatial prepositions, object-specific function and task requirements. Journal of Semantics 11, 289–309 (1994)
Auer, P., et al.: A Research Roadmap of Cognitive Vision, ECVision: European Network for Research in Cognitive Vision Systems (19.04.2005), http://www.eucognition.org/ecvision/research_planning/ECVisionRoadmapv5.0.pdf
Freksa, C.: Using orientation information for qualitative spatial reasoning. In: Frank, A.U., Campari, I., Formentini, U. (eds.) Spatio-Temporal Reasoning, pp. 162–178 (1992)
Gómez-Pérez, A., Fernández-López, M., Corcho, C.: Ontological Engineering with examples from the areas of Knowledge Management, e-Commerce and the Semantic Web. Springer, Heidelberg (2004)
Gärdenfors, P.: Conceptual Spaces: The Geometry of Thought. A Bradford Book. MIT Press, Cambridge (2000)
Hernández, D.: Qualitative Representation of Spatial Knowledge. In: Hernández, D. (ed.) Qualitative Representation of Spatial Knowledge. LNCS, vol. 804, Springer, Heidelberg (1994)
Herskovits, A.: Language and Spatial Cognition: an interdisciplinary study of the prepositions in English. Studies in Natural Language Processing (1986)
Hois, J., Schill, K., Bateman, J.A.: Integrating Uncertain Knowledge in a Domain Ontology for Room Concept Classifications. In: Bramer, M., Coenen, F., Tuson, A. (eds.) The Twenty-sixth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence. Research and Development in Intelligent Systems, Springer, Heidelberg (2006)
Krebs, B., Burkhardt, M., Wahl, F.M.: Integration of Multiple Feature Detection by a Bayesian Net for 3D Object Recognition. Mustererkennung, pp. 143–150 (1998)
Levinson, S.C.: Space in Language and Cognition. Cambridge University Press, Cambridge (2003)
Masolo, C., Borgo, S., Gangemi, A., Guarino, N., Oltramari, A.: Ontologies library (final). WonderWeb Deliverable D18, ISTC-CNR, Padova, Italy (December 2003)
Matsakis, P., Keller, J., Wendling, L., Marjamaa, J., Sjahputera, O.: Linguistic description of relative positions in images. IEEE Transactions on Systems, Man and Cybernetics, Part B 4(32), 573–588 (2001)
Meyer, A.: Merkmals- und formbasierte 3D-Objekterkennung für Büroszenen. Diplomarbeit, Universität Bremen (2005)
Moratz, R., Tenbrink, T.: Spatial reference in linguistic human-robot interaction: Iterative, empirically supported development of a model of projective relations. Spatial Cognition and Computation 6(1), 63–106 (2006)
Moratz, R., Tenbrink, T., Bateman, J.A., Fischer, K.: Spatial knowledge representation for human-robot interaction. In: Freksa, C., Brauer, W., Habel, C., Wender, K.F. (eds.) Spatial Cognition III. LNCS (LNAI), vol. 2685, pp. 263–286. Springer, Heidelberg (2003)
Nagel, H.-H.: Steps toward a Cognitive Vision System. AI Magazine 25(2), 31–50 (2004)
Nagel, H.-H.: Cognitive Vision Systems (CogViSys) (31.08.2001), http://cogvisys.iaksuni-karlsruhe.de/homepage_CogViSys_V3B.html
Roy, D., Gorniak, P., Mukherjee, N., Juster, J.: A Trainable Spoken Language Understanding System For Visual Object Selection. In: International Conference of Spoken Language Processing (2002)
Stamos, I., Allen, P.K.: 3-D Model Construction using Range and Image Data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition CVPR 2000, pp. 531–536. IEEE, Los Alamitos (2000)
Tenbrink, T.: Semantics and Application of Spatial Dimensional Terms in English and German. SFB/TR8 internal report I1-[OntoSpace], Collaborative Research Center for Spatial Cognition, University of Bremen, University of Freiburg, Germany (2005)
Wünstel, M., Röfer, T.: A Probabilistic Approach for Object Recognition in a Real 3-D Office Environment. In: WSCG 2006 Posters Proceedings (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hois, J., Wünstel, M., Bateman, J.A., Röfer, T. (2007). Dialog-Based 3D-Image Recognition Using a Domain Ontology. In: Barkowsky, T., Knauff, M., Ligozat, G., Montello, D.R. (eds) Spatial Cognition V Reasoning, Action, Interaction. Spatial Cognition 2006. Lecture Notes in Computer Science(), vol 4387. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75666-8_7
Download citation
DOI: https://doi.org/10.1007/978-3-540-75666-8_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75665-1
Online ISBN: 978-3-540-75666-8
eBook Packages: Computer ScienceComputer Science (R0)