Functional parts detection in engineering drawings: Looking for the screws

  • María A. Capellades
  • Octavia I. Camps
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1072)


Functional parts — i.e. mechanical parts with intrinsic functionality — such as screws, hinges and gears, are appealing high level entities to be used in line drawing understanding systems. This is because their functionality can be used by a reasoning agent to infer surrounding objects and because they are usually drawn following standards making them easier to be detected. In this chapter, an algorithm for the automatic detection of the schematic representation of screws in mechanical engineering drawings is being presented as a first step towards a function-based line drawing understanding system. All the running parameters required by the algorithm are set according to the American National Standards Institute standards and by using a rigorous experimental protocol characterizing the algorithm performance in the presence of image degradation, thus eliminating the need for ad hoc parameter tuning. Experimental results on several real line drawings are also presented.


False Alarm False Alarm Rate Real Image Functional Part Image Degradation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    ANSI Y14.6: Screw Thread Representation. American National Institute of Standards, (1983)Google Scholar
  2. 2.
    Brady, M., Agre, P. E., Braunegg, D. J., Connell, J. H.: The mechanics mate. In T. O'Shea, editor, Advances in Artificial Intelligence, pages 79–94. Elsevier, New York (1985)Google Scholar
  3. 3.
    Di Manzo, M., Trucco, E., Giunchiglia, F., Ricci, F.: FUR: Understanding FUnctional Reasoning. International Journal on Intelligent Systems, 4 (1989) 159–183Google Scholar
  4. 4.
    Dori, D., Liang, Y., Dowell, J., Chai, I.: Sparse-Pixel Recognition of Primitives in Mechanical Engineering Drawings. Machine Vision and Applications, 6 (1993) 69–82Google Scholar
  5. 5.
    Haralick, R.: Performance Characterization Protocol in Computer Vision. Proc. of NFS/ARPA Workshop on Performance versus Methodology in Computer Vision, June (1994)Google Scholar
  6. 6.
    Ho, S.: Representing and using functional definitions for visual recognition. PhD thesis, University of Wisconsin, Madison, WI (1987)Google Scholar
  7. 7.
    IEEE Computer Society Technical Committee on Pattern Analysis and Machine Intelligence. IEEE Computer Society Workshop on the Role of Functionality in Object Recognition. june (1994).Google Scholar
  8. 8.
    Joseph, S. H., Pridmore, T. P.: Knowledge-Directed Interpretation of Mechanical Engineering Drawings. IEEE Trans. on Pattern Analysis and Machine Intelligence, 14(9) (1992) 928–940Google Scholar
  9. 9.
    Kanungo, T., Haralick, R. M., Phillips, I.: Global and Local Document Degradation Models. Proc. of Second International Conference on Document Analysis and Recognition, October (1993) 20–22Google Scholar
  10. 10.
    Kanungo, T., Jaisimha, M. Y., Palmer, J., Haralick, R.: A Methodology for Analyzing the Performance of Detection Algorithms. Proc. of the 4th International Conference on Computer Vision, May (1993) 247–252Google Scholar
  11. 11.
    Kernighan, B. W., Ritchie, D. M.: The C Programming Language. Prentice Hall, Murray Hill, NJ (1988)Google Scholar
  12. 12.
    Luzadder, W. J.: Fundamentals of Engineering Drawing. 8th edition, Prentice Hall, Englewood Cliffs, NJ (1981)Google Scholar
  13. 13.
    Luzadder, W. J., Duff, J. M.: Fundamentals of Engineering Drawing. Prentice Hall, Englewood Cliffs, NJ (1993)Google Scholar
  14. 14.
    Minsky, M.: The Society of Mind. Simon and Schuster, New York (1985)Google Scholar
  15. 15.
    Rosch, E., Mervis, C. B., Gray, W. D., Johnson, D., Boyes-Braem, P.: Basic objects in natural categories. Cognitive Psychology, 8 (1976) 382–439Google Scholar
  16. 16.
    Sedgewick, R.: Algorithms. Addison-Wesley Publishing Co., New York (1988)Google Scholar
  17. 17.
    Stark, L., Bowyer, K.: Achieving generalized object recognition through reasoning about association of function to structure. IEEE Transactions on Pattern Analysis and Machine Intelligence, October (1991) 1097–1104Google Scholar
  18. 18.
    Vaxiviére, P., Tombre, K.: CELESSTIN: A System for Conversion of Mechanical Engineering Drawings into CAD format. IEEE Computer: Special Issue on Document Image Analysis Systems, July (1992) 46–54Google Scholar
  19. 19.
    Winston, P. H.: Learning structural descriptions from examples. In P. H. Winston, editor, The Psychology of Computer Vision. McGraw-Hill, New York (1975)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • María A. Capellades
    • 1
  • Octavia I. Camps
    • 1
    • 2
  1. 1.Dept. of Electrical EngineeringThe Pennsylvania State UniversityUniversity Park
  2. 2.Dept. of Computer Science and EngineeringThe Pennsylvania State UniversityUniversity Park

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