Abstract
This paper presents the framework of the new context-based reasoning components of the GRUFF (Generic Recognition Using Form and Function) system. This is a generic object recognition system which reasons about and generates plans for understanding 3-D scenes of objects. A range image is generated from a stereo image pair and is provided as input to a multi-stage recognition system. A 3-D model of the scene, extracted from the range image, is processed to identify evidence of potential functionality directed by contextual cues. This recognition process considers the shape-suggested functionality by applying concepts of physics and causation to label an object’s potential functionality. The methodology for context-based reasoning relies on determining the significance of the accumulated functional evidence derived from the scene. For example, functional evidence for a chair or multiple chairs along with a table, in set configurations, is used to infer the existence of scene concepts such as “office” or “meeting room space.” Results of this work are presented for scene understanding derived from both simulated and real sensors positioned in typical office and meeting room environments.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Binford, T. O.: Survey of model-based image analysis systems, Int. J. of Robotics Research, 1, (1982) 18–64
Bogoni, L. and Bajcsy, R.: Interactive Recognition and Representation of Functionality, Computer Vision and Image Understanding, special issue on Functionality in Object Recognition, Vol. 62, No. 2, (1995) 194–214
Connell, J. H.: Get Me That Screwdriver! Developing a Sensory-action Vocabulary for Fetch-and-Carry Tasks, IBM Cyber Journal Research Report, RC 19473 (April 1994)
Cooper, P., Birnbaum, L., and Brand, E.: Causal Scene Understanding, Computer Vision and Image Understanding, special issue on Functionality in Object Recognition, Vol. 62, No. 2, (1995) 215–231
Di Manzo, M., Trucco, E., Giunchiglia, F., Ricci, F.: FUR: Understanding FUnctional Reasoning, Int. J. of Intelligent Systems, 4, (1989) 431–457
Doermann, D., Rivlin, E. and Rosenfeld, A.: The Function of Documents, Int. J. on Computer Vision, 16, (1998) 799–814
Duric, Z., Fayman, J. A., and Rivlin, E.: Function from Motion, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 18, No. 6, (1996) 579–591
Fischler, M. A. and Strat, T. M.: Recognizing objects in a natural environment: a contextual vision system (CVS), Proceedings: Image Understanding Workshop, (May 1989) 774–788
Green, K., Eggert, D., Stark, L. and Bowyer, K.: Generic Recognition of Articulated Objects through Reasoning about Potential Function, Computer Vision and Image Understanding, special issue on Functionality in Object Recognition, Vol. 62, No. 2, (1995) 177–193
Hodges, J.: Functional and Physical Object Characteristics and Object Recognition in Improvisation, Computer Vision and Image Understanding, special issue on Functionality in Object Recognition, Vol. 62, No. 2, (1995) 147–163
Hoogs, A. and Hackett, D.: Model-Supported Exploitation as a Framework for Image Understanding, Proceedings of the ARPA IU Workshop, (November 1994) 265–268
Hoover, A., Goldgof, D. and Bowyer, K. W.: Building a B-rep from a segmented range image, IEEE Second CAD-Based Vision Workshop, Champion, Pennsylvania (February 1994), 74–81
Jensen, F., Christensen, H. and Nielsen, J.: Bayesian Methods for Interpretation and Control in Multi-agent Vision Systems, in Proceedings of SPIE Conference on Application of AI X: Machine Vision and Robotics, Kevin W. Bowyer, editor, Vol. 1708, (1992) 536–548
Kim, D. and Nevatia, R.: A Method for Recognition and Localization of Generic Objects for Indoor Navigation, IEEE Workshop on Applications of Computer Vision, (1994), 280–288
Kise, K., Hattori, H., Kitahashi, T., and Fukunaga, K.: Representing and Recognizing Simple Hand-tools Based on Their Functions, Asian Conference on Computer Vision, Osaka, Japan (November, 1993), 656–659
Konolige, K., Beymer, D.: SRI small vision system user’s manual (software version 1.4). Stanford Research Institute. (December 1999)
Hanson, A. and Riseman, E.: The VISIONS Image-Understanding System Advances in Computer Vision (2 vols), Erlbaum, Vol. 1, (1988), 1–114
Stark, L., Hoover, A. W., Goldgof, D. B. and Bowyer, K. W.: Function-based object recognition from incomplete knowledge of object shape, IEEE Workshop on Qualitative Vision, New York (June 1993), 11–22
Minsky, M.: The Society of Mind, Simon and Shuster, New York, (1985)
Rivlin, E., Dickinson, S. and Rosenfeld, A.: Recognition by Functional Parts, Computer Vision and Image Understanding, special issue on Functionality in Object Recognition, Vol. 62, No. 2, (1995) 164–176
Rivlin, E. and Rosenfeld, A.: Navigational Functionalities, Computer Vision and Image Understanding, special issue on Functionality in Object Recognition, Vol. 62, No. 2, (1995) 232–244
Rivlin, E., Rosenfeld, A. and Perlis, D.: Recognition of Object Functionality in Goal-Directed Robotics, in Working Notes on Reasoning About Function, (1993) 126–130
Stark, L. and Bowyer, K.: Generic Object Recognition using Form and Function, Series in Machine Perception Artificial Intelligence, Vol. 10, World Scientific, New York, (1996)
Stark, L., and Bowyer, K. W.: Achieving generalized object recognition through reasoning about association of function to structure, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 3, No. 10, (1991) 1097–1104
Stark, L., and Bowyer, K. W.: Indexing function-based categories for generic object recognition, Computer Vision and Pattern Recognition (CVPR’ 92), Champaign, Illinois (June 1992) 795–797
Stark, L. and Bowyer, K.: Function-based generic recognition for multiple object categories, CVGIP: Image Understanding, 59 (1), (January 1994) 1–21
Stark, L., Hall, L. O. and Bowyer, K. W.: An investigation of methods of combining functional evidence for 3-D object recognition, International Journal of Pattern Recognition and Artificial Intelligence 7 (3), (June 1993) 573–594
Stark, L., and Bowyer, K. W.: Functional Context in Vision, Proceedings of the Workshop on Context-Based Vision, Cambridge, Massachusetts, (1995) 63–74
Strat, T. M. and Fischler, M. A.: The Role of Context in Computer Vision, Proceedings of the Workshop on Context-Based Vision, Cambridge, Massachusetts, (1995) 2–12
Sutton, M., Stark, L., Bowyer, K. W.: Function from visual analysis and physical interaction: A methodology for recognition of generic classes of objects. Image and Vision Computing. 16 (11) (August 1998) 745–763
Stansfield, R. A.: Robotic grasping of unknown objects: A knowledge-based approach, Int. Journal of Robotics Research, 10, (1991) 314–326
Sutton, M., Stark, L. and Bowyer, K. W.: Function-based generic recognition for multiple object categories, in Three-dimensional Object Recognition Systems, A. K. Jain and P. J. Flynn, editors, Elsevier Science Publishers, (1993) 447–470
Weisbin, C. R., et al.: Autonomous mobile robot navigation and learning, IEEE Computer, Vol. 22, (1989) 29–35
Winston, P., Binford, T., Katz, B., and Lowry, M.: Learning Physical Description from Functional Deffinitions, Examples, and Precedents, AAAI’ 83 (1983) 433–439
Winston, P. and Rao, S.: Repairing learned knowledge using experience, in AI at MIT: Expanding Frontiers, P. H. Winston and S. A. Shellard, editors, MIT Press (1990) 363–379
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sutton, M.A., Stark, L., Hughes, K. (2002). Exploiting Context in Function-Based Reasoning. In: Hager, G.D., Christensen, H.I., Bunke, H., Klein, R. (eds) Sensor Based Intelligent Robots. Lecture Notes in Computer Science, vol 2238. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45993-6_20
Download citation
DOI: https://doi.org/10.1007/3-540-45993-6_20
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43399-6
Online ISBN: 978-3-540-45993-4
eBook Packages: Springer Book Archive