Skip to main content
SpringerLink
Log in

A knowledge-based vision system for industrial applications

  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

This paper introduces a knowledge-based vision system for industrial environments. It is designed to control a cell in an assembly system. The images from the environment are taken as gray scale images. Based on a single image, the system has to recognize type and position of the recorded parts and to control their placement in the environment for further manipulation. This requires the explicit representation of rich task-specific knowledge. The effort to adapt our system to new tasks is very small. Thus, it is very important that the system is able to support major parts of the activities that are necessary for the acquisition of new knowledge. The system consists of three components-image segmentation, knowledge acquisition, and matching-which are described in detail. All the methods presented were tested using different parts of an electric motor as an example.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Agin G (1980) Computer vision systems for industrial inspection and assembly. IEEE Transactions on Computers 13:11–1201.

    Google Scholar 

  • Agin G, Binford T (1976) Computer descriptions of curved objects. IEEE Transactions on Computers 25:439–449.

    Google Scholar 

  • Ayache N (1983) A model-based vision system to identify and locate partially visible industrial parts. In-Proceedings of Computer Vision and Pattern Recognition, pp. 492–494.

  • Ballard D, Brown C (1982) Computer vision. Prentice-Hall, Englewood Cliffs, NJ.

    Google Scholar 

  • Batchelor B, Hill D, and Hodgson D (Eds) (1985) Automated visual inspection. IFS Ltd, Bedford, UK.

    Google Scholar 

  • Bowker K (1974) Edge vector image analysis. In: Proceedings of the 2nd International Joint Conference on Pattern Recognition, IEEE, Copenhagen, pp. 520–524.

    Google Scholar 

  • Brachman R (1977) What's in a concept: Structural foundations for semantic networks. BBN Report, Bolt Beranek & Newman, Cambridge, MA.

    Google Scholar 

  • Brachman J (1979) On the epistemological status of semantic networks. In: Findler N. (ed) Associative networks, Academic Press, New York. pp. 3–50.

    Google Scholar 

  • Brachman R, Schmolze I (1985) An overview of the KLONE knowledge representation language. Cognitive Science 9:171–216.

    Google Scholar 

  • Brice C, Fennema C (1970) Scene analysis using regions. Artificial Intelligence 1:205–226.

    Google Scholar 

  • Brooks R (1981) Symbolic reasoning among 3-D models and 2-D images. Artificial Intelligence 17:285–341.

    Google Scholar 

  • Brünig H, Niemann H (1988) IPAX: Konzeption und Realisierung eines Bildverarbeitungssystems In: Proceedings of 10. DAGM-Symposium Mustererkennung, Springer-Verlag, Berlin, pp. 60–67.

    Google Scholar 

  • Canny J (1986) A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(6):679–698.

    Google Scholar 

  • Cheriton D, Tarjan R (1976) Finding minimum spanning trees. SIAM Journal on Computing 5(4):724–742.

    Google Scholar 

  • Chiyokura H (1988) Solid modelling with designbase. Addison-Wesley, Singapore.

    Google Scholar 

  • Cohen F, Cayula JP (1984) 3-D object recognition from a single image. SPIE, Intelligent Robots and Computer Vision 521:7–15.

    Google Scholar 

  • Darpa (1987) Proceedings of Image Understanding Workshop. Morgan Kaufmann, Los Angeles.

    Google Scholar 

  • Davis L, Rosenfeld A (1978) Noise cleaning by iterated local averaging. IEEE Transactions on Systems, Man, and Cybernetics 8(9):705–710.

    Google Scholar 

  • Dietterich T, Michalsky R (1981) Inductive learning of structural descriptions. Artificial Intelligence 16:257–294.

    Google Scholar 

  • Duda R, Hart P (1972) Pattern classification and scene analysis. John Wiley, New York.

    Google Scholar 

  • Eberlein R (1976) An iterative gradient edge detection algorithm. Computer Graphics and Image Processing 5:245–253.

    Google Scholar 

  • Farin G (Ed) (1987) Geometric modelling. SIAM, Philadelphia.

    Google Scholar 

  • Feldmann K (1987a) Rechnerintegrierte montagesysteme. In: Feldmann K, Geiger M, Herzog U, Neimann H, Schmidt B, and Wedekind H (eds) Proceedings of Fachtagung Rechnerintegrierte Produktionssysteme, Erlangen, pp. 279–294.

  • Feldmann K (1987b) Ziele, aufbau und arbeitsweise des koorperationsprojektes PAP. In: Feldmann K, Geiger M, Herzog U, Niemann H, Schmidt B, and Wedekind H (eds) Proceedings of Fachtagung Rechnerintegrierte Produktionssysteme, Erlangen, pp. 29–41.

  • Feldmann K, Geiger M, Herzog U, Niemann H, Schmidt B, and Wedekind H (1987c) Proceedings of Fachtagung Rechnerintegrierte Produktionssysteme, Erlangen.

  • Findler N (1979) Associative networks. Academic Press, New York.

    Google Scholar 

  • Frankot R, Chellappa R (1987) A method for enforcing integrability in shape from shading algorithms. In: Proceedings of First International Conference on Computer Vision (ICCV), London pp. 118–127.

  • Frei W, Chen C (1977) Fast boundary detection: A generalization and a new algorithm. IEEE Transactions on Computers 26:988–998.

    Google Scholar 

  • Heß P.:(1986) Dreidimensional modellbildung in der bildanalyse. Dissertation, Universität Erlangen-Nürnberg, Erlangen.

    Google Scholar 

  • Horowitz S, Pavlidis T (1974) Picture segmentation by a directed split-and-merge procedure. In: Proceedings of 2nd International Joint Conference on Pattern Recognition, Kopenhagen pp. 424–433.

  • Horowitz S, Pavlidis T (1976) Picture segmentation by a tree traversal algorithm, Journal of Associations for Computing Machinery 23:368–388.

    Google Scholar 

  • Hueckel M (1973) A local visual operator which recognizes edges and lines. Journal of Associations for Computing Machinery 18:634–647; erratum in 21:350, 1974.

    Google Scholar 

  • Kirsch R (1971) Computer determination of the constituent structure of biological images. Computers and Biomedical Research 4:315–328.

    Google Scholar 

  • Kowalski R (1974) Predicate logic as a programming language. Information Processing 74:569–574.

    Google Scholar 

  • Kümmert F, Niemann H, Sagerer G, Schröder S (1987) Werkzeuge zur modellgesteuerten bildanalyse und wissensakquisition-Das system ERNEST. In: M. Paul (ed) GI-17. Jahrestagung Computerintegrierter Arbeitsplatz in Büro, Springer Verlag, Berlin, pp. 556–570.

    Google Scholar 

  • Lee C, Rosenfeld A (1985) Improved methods of estimating shape from shading using the light source coordinate system. Artifical Intelligence 26:125–143.

    Google Scholar 

  • Levesque H (1977) A procedural approach to semantic networks. Tchnical Report 105, University of Toronto, Department of Computer Science.

  • Levesque H, Mylopoulos J (1979) A procedural semantics for semantic networks. In: Findler NV (ed) Associative networks, Academic Press, New York, pp. 93–121.

    Google Scholar 

  • Mero L, Vassy Z (1978) A simplified and fast version of the Hueckel operator for finding optimal edges in pictures. In: Proceedings of 4th International Conferences on Artificial Intelligence, Tbilisi, USSR, pp. 650–655.

  • Muerle J, Allen D (1968) Experimental evaluation of techniques for automatic segmentation of objects in a complex scene. In: Cheng G, Ledley R, Pollock D, Rosenfeld A (eds) Pictoral pattern recognition, S. Tompson, Washington, DC, pp. 3–13.

    Google Scholar 

  • Mylopoulos J, Levesque (1983) An overview of knowledge representation. In: Brodie M, Mylopoulos J, Schmidt J (eds) On conceptual modelling: Perspectives from artificial intelligence. Springer-Verlag, New York, pp. 3–17.

    Google Scholar 

  • Nagao M, Matsuyama T (1979) Edge preserving smoothing. Computer Graphics and Image Processing 9:394–407.

    Google Scholar 

  • Nevada R, Babu R (1980) Linear feature extraction and description. Computer Graphics and Image Processing 13:257–269.

    Google Scholar 

  • Niemann H (1981) Pattern analysis. Springer-Verlag, Berlin.

    Google Scholar 

  • Niemann H (1985) A homogeneous architecture for knowledge based image understanding. In: Proceedings of 2nd Conference on Artificial Intelligence Applications, Miami, FL pp. 88–93.

  • Niemann H (1987) Wissensbasierte Verarbeitung industrieller szenen. In: Feldmann K, Geiger M, Herzog U, Niemann H, Schmidt B, and Wedekind H (eds) In: Proceedings of Fachtagung Rechnerintegrierte Produktionssysteme, Erlange, pp. 133–147.

  • Nilsson N (1982) Principles of artificial intelligence. Springer-Verlag, Berlin.

    Google Scholar 

  • Ohlander R, Price K, and Reddy D (1978) Picture segmentation using a recursive region splitting method. Computer Graphics and Image Processing 8:313–333.

    Google Scholar 

  • Ortega J. Rheinbold W (1970) Iterative solution of nonlinear equations in several variables. Computer science and applied mathematics. Academic Press, New York.

    Google Scholar 

  • Paler K, Kittler J (1983) Gray level edge thinning: A new method. Pattern Recognition Letters 1:409–416.

    Google Scholar 

  • Paulus D (1989) Datenstrukturen für die symbolische Bildverarbeitung. Interner Bericht 2, Lehrstuhl für Informatik 5 (Mustererkennung), University of Erlangen-Nürnberg, West Germany.

    Google Scholar 

  • Pavlidis T (1977) Structural pattern recognition. Spinger-Verlag, Berlin.

    Google Scholar 

  • Perkins W (1978) A model-based vision system for industrial parts. IEEE Transactions on Computers C-27(2):126–143.

    Google Scholar 

  • Picket M, Boyse J (eds) (1984) Solid modelling by computers. Plenum Press, New York.

    Google Scholar 

  • Pohlmann G (1982) Rechnerintegrierte Objektdarstellung als basis integrierter CAD-systeme. Vol. 27 von Reihe Produktionstechnik Berlin, Carl Hanser Verlag, München.

    Google Scholar 

  • Prager M (1980) Extracting and labeling boundary segments in natural scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence 2(1):16–27.

    Google Scholar 

  • Prewitt J (1970) Object enhancement and extraction. Picture Processing and Psychopictorics 75–149.

  • Requicha A, Voelcker H (1982) Solid modelling: A historical summary and contemporary assessment. IEEE Computer Graphics and Applications 2:9–26.

    Google Scholar 

  • RFC external data representation standard: Protocol specifications. (1988) Sun Microsystems Inc., Standford, SUN OS 4 Manuals, Network Programming, part 2 ed.

  • Roberts L (1965) Machine perception of three dimensional solids. Optical Electro-Optical Processing of Information pp. 159–197.

  • Robertson T, Swain P, and Fu K (1973) Multispectral image partitioning. TR-EE 73-26, LARS Inf. Note 071373, School of El. Eng., Purdue University, Lafayette, IN.

    Google Scholar 

  • Robinson G (1977) Edge detection by compass gradient masks. Computer Graphics and Image Processing 6:492–501.

    Google Scholar 

  • Rosenfeld A, Johnston E (1973) Angle detection on digital curves. IEEE Transactions on Computers 22(9):875–878.

    Google Scholar 

  • Rosenfeld A, Thurston M (1971) Edge and curve detection for visual scene analysis. IEEE Transactions on Computers 20(5):562–569.

    Google Scholar 

  • Rummel P (1984) A model based visual sensor system for complex industrial scenes. Siemens Forschungsund Entwicklungsberichte 13(3):151–154.

    Google Scholar 

  • Rummel P, Beutel W (1984) Workpiece recognition and inspection by a model based scene analysis system. Pattern Recognition 17(1):141–148.

    Google Scholar 

  • Sagerer G (1985) Darstellung und nutzung von expertenwissen für ein bildanaylsesystem. Informatik fachberichte, Springer-Verlag, Berlin.

    Google Scholar 

  • Sagerer G, Kummert F (1988) Knowledge based systems for speech understanding. In: Niemann H, Lang M, and Sagerer G (eds) Recent advances in speech understanding and dialog systems, Springer-Verlag, Berlin, pp. 421–458.

    Google Scholar 

  • Schröder S, Niemann H, and Sagerer G (1988) Knowledge acquisition for a knowledge based image analysis system. In: Proceedings European Knowledge Acquisition Workshop (EKAW88), Gesellschaft für Mathematik und Datenverarbeitung mbH, Bonn pp. 29/1–29/15.

    Google Scholar 

  • Shani U (1980) A 3-D model-driven system for the recognition of abdominal anatomy from CT scans. In: Proceedings of 5th International Joint Conference on Pattern Recognition, pp. 585–591.

  • Spur G, Krause F (1984) CAD-Technik Carl Hauser Verlag, FDG.

  • Suk M, Song O (1984) Curvilinear feature extraction using minimum spanning trees. Computer Vision, Graphics and Image Processing 26:400–411.

    Google Scholar 

  • Tamura H, et al. (1983) Design and implementation of SPIDER-A transportable image processing software package. Computer Vision, Graphics and Image Processing 23:273–294.

    Google Scholar 

  • Walther N (1987) Graphen, algorithmen, programme, Springer-Verlag, Berlin.

    Google Scholar 

  • Woods W (1975) What's in a link? Foundations for semantic networks. In Bobrow D, Collins A (eds): Representation and understanding. Academic Press, New York.

    Google Scholar 

  • Yashida M, Tsuji S (1977) A versatile machine vision system for complex industrial parts. IEEE Transactions on Computers C26:882–894.

    Google Scholar 

  • Zadeh L, Fu K, Tanaka K, and Shimura M (eds) (1975) Fuzzy sets and their application to cognitive and decision processes. Academic Press, New York.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Lehrstuhl für Informatik 5 (Mustererkennung), Universität Erlangen-Nürnberg, Martensstraße 3, 8520, Erlangen, FR of Germany

    H. Niemann, H. Brünig, R. Salzbrunn & S. Schröder

Authors
  1. H. Niemann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. H. Brünig
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R. Salzbrunn
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S. Schröder
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Niemann, H., Brünig, H., Salzbrunn, R. et al. A knowledge-based vision system for industrial applications. Machine Vis. Apps. 3, 201–229 (1990). https://doi.org/10.1007/BF01211848

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01211848

Key words

Search

Navigation