Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30685
Consistent Modeling of Functional Dependencies along with World Knowledge

Authors: Sven Rebhan, Nils Einecke, Julian Eggert

Abstract:

In this paper we propose a method for vision systems to consistently represent functional dependencies between different visual routines along with relational short- and long-term knowledge about the world. Here the visual routines are bound to visual properties of objects stored in the memory of the system. Furthermore, the functional dependencies between the visual routines are seen as a graph also belonging to the object-s structure. This graph is parsed in the course of acquiring a visual property of an object to automatically resolve the dependencies of the bound visual routines. Using this representation, the system is able to dynamically rearrange the processing order while keeping its functionality. Additionally, the system is able to estimate the overall computational costs of a certain action. We will also show that the system can efficiently use that structure to incorporate already acquired knowledge and thus reduce the computational demand.

Keywords: Systems Engineering, Knowledge Representation, Adaptive systems, Machinevision

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1330693

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1366

References:


[1] Julian Eggert, Sven Rebhan, and Edgar K¨orner. First steps towards an intentional vision system. In Proceedings of the 5th International Conference on Computer Vision Systems (ICVS), 2007.
[2] Jack B. Dennis. First version of a data flow procedure language. In Proceedings of the Colloque sur la Programmation, volume 19 of Lecture Notes in Computer Science, pages 362-376, London, UK, 1974. Springer-Verlag.
[3] Jack B. Dennis. Data flow supercomputers. Computer, 13(11):48-56, November 1980.
[4] Jeanne Ferrante, Karl J. Ottenstein, and Joe D. Warren. The program dependence graph and its use in optimization. ACM Transactions on Programming Language and Systems, 9(3):319-349, July 1987.
[5] Robert Cartwright and Matthias Felleisen. The semantics of program dependence. In Proceedings of the ACM SIGPLAN 89 Conference on Programming Language Design and Implementation, pages 13-27, 1989.
[6] Per Andersson. Modelling and implementation of a vision system for embedded systems, 2003.
[7] Florian R¨ohrbein, Julian Eggert, and Edgar K¨oerner. Prototypical relations for cortex-inspired semantic representations. In Proceedings of the 8th International Conference on Cognitive Modeling (ICCM), pages 307-312. Psychology Press, Taylor & Francis Group, 2007.
[8] Robert A. Ballance, Arthur B. Maccabe, and Karl J. Ottenstein. The program dependence web: A representation supporting control-, data-, and demand-driven interpretation of imperative languages. In Proceedings of the ACM SIGPLAN 90 Conference on Programming Language Design and Implementation, volume 25, pages 257-271, New York, NY, USA, 1990. ACM.
[9] Sven Rebhan, Florian R¨ohrbein, Julian Eggert, and Edgar K¨orner. Attention modulation using short- and long-term knowledge. In A. Gasteratos, M. Vincze, and J.K. Tsotsos, editors, Proceeding of the 6th International Conference on Computer Vision Systems (ICVS), LNCS 5008, pages 151-160. Springer Verlag, 2008.
[10] Milan Sonka, Vaclav Hlavac, and Roger Boyle. Image Processing, Analysis, and Machine Vision. Thomson-Engineering, 2 edition, 1998.
[11] Daniel Weiler and Julian Eggert. Multi-dimensional histogram-based image segmentation. In Proceedings of the 14th International Conference on Neural Information Processing (ICONIP), pages 963-972, 2007.