Advertisement

Contour Description Through Set Operations on Dynamic Reference Shapes

  • Miroslav Koprnicky
  • Maher Ahmed
  • Mohamed Kamel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3211)

Abstract

Eight novel features for irregular shape classification which use simple set operations to compare contours to regularized reference shapes are introduced. The features’ intuitive simplicity and computational efficiency make them attractive choices for real time shape analysis problems such as defect inspection. Performance is evaluated through a brute force feature combination search, in which KNN classification rates of the proposed features are compared to several existing features also based on contour comparison. Results indicate that combinations of the proposed features consistently improve classification rates when used to supplement the feature set. Efficacy of the individual features ranges greatly, but results are promising, especially for Outer Elliptical Variation; its strong performance, in particular, calls for further investigation.

Keywords

Classification Error Rate Reference Shape Circular Variance Dynamic Reference Defect Inspection 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Batchelor, B.G., Whelan, P.F.: Intelligent Vision Systems for Industry. Springer, London (1997)Google Scholar
  2. 2.
    Locaric, S.: A Survey of Shape Analysis Techniques. Pattern Recognition 31(8), 983–1001 (1998)CrossRefGoogle Scholar
  3. 3.
    Peura, M., Iivarinen, J.: Efficiency of simple shape descriptors. In: Arcelli, C., Cordella, L.P., Sanniti di Baja, G. (eds.) Aspects of visual form processing, pp. 443–451. World Scientific, Singapore (1997)Google Scholar
  4. 4.
    Iivarinen, J., Visa, A.: An Adaptive Texture and Shape Based Defect Classification. In: Proc. International Conf. on Pattern Recognition, pp. 117–123 (1998)Google Scholar
  5. 5.
    Kohonen, T.: Self-Organization and Associative Memory, 3rd edn. Springer Series in Information Sciences. Springer, Heidelberg (1989)Google Scholar
  6. 6.
    James, M.: Pattern Recognition. BSP Professional Books, Oxford (1987)Google Scholar
  7. 7.
    Duin, B.: PRTools Version 3.0, Pattern Recognition Group, Delft University of Technolgy, P.O. Box 5046, 2600 GA Delft, The Netherlands (2000), http://www.ph.tn.tudelft.nl/~bob/PRTOOLS.html

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Miroslav Koprnicky
    • 1
  • Maher Ahmed
    • 2
  • Mohamed Kamel
    • 1
  1. 1.Pattern Analysis and Machine Intelligence LaboratoryUniversity of WaterlooWaterlooCanada
  2. 2.Department of Physics and ComputingWilfrid Laurier UniversityWaterlooCanada

Personalised recommendations