Skip to main content
SpringerLink
Log in

Fourier-Based Object Description in Defect Image Retrieval

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

Abstract

Image retrieval has nowadays several industrial applications. In these imaging applications, which typically use large image archives, the matter of computational efficiency is essential. Therefore, compact and efficient features are required to describe the visual content of the images. In this paper, we introduce a new Fourier-based object descriptor that combines the shape and color information of the objects occurring in images into a single low-dimensional descriptor. The experiments performed with an industrial surface defect image database show that the proposed descriptor is an accurate and computationally light approach to object description in a real-world retrieval problem.

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

  1. Baeza-Yates R., Ribeiro-Neto B. (1999). Modern Information Retrieval. ACM Press, Addison-Wesley, New York

    Google Scholar 

  2. Bigun J. (2005). Vision with direction. Springer, Berlin Heidelberg New York

    MATH  Google Scholar 

  3. Del Bimbo A. (2001). Visual Information Retrieval. Morgan Kaufmann Publishers, San Fransisco

    Google Scholar 

  4. Dumitra A., Venetsanopoulos A.N. (2001). Angular map-driven snakes with application to object shape description in color images. IEEE Trans. Image Process. 10(12): 1851–1859

    Article  Google Scholar 

  5. Gevers T., Smeulders A.W.M. (2000). PicToSeek: combining color and shape invariants features for image retrieval. IEEE Trans. Image Process 9(1): 102–119

    Article  Google Scholar 

  6. Granlund G.H. (1972). Fourier preprocessing for hand print character recognition. IEEE Trans Compute. C-21(2): 195–201

    Article  MathSciNet  Google Scholar 

  7. Hsieh I.-S., Fan K.-C. (2001). Multiple classifiers for color flag and trademark image retrieval. IEEE Trans. Image Process. 10(6): 938–950

    Article  MATH  Google Scholar 

  8. Iivarinen, J., Rauhamaa, J., Visa, A.: Unsupervised segmentation of surface defects. In: Proceedings of the 13th International Conference on Pattern Recognition, vol. 4, pp. 356–360 (1996)

  9. Kauppinen H., Seppänen T., Pietikäinen M. (1995). An experimental comparison of autoregressive and Fourier-based descriptors in 2D shape classification. IEEE Trans. Pattern Anal. Mach. Intell. 17(2): 201–207

    Article  Google Scholar 

  10. Kim H., Kim J. (2000). Region-based shape descriptor invariant to rotation, scale and translation. Signal Process. Image Commun. 16(1): 87–93

    Article  Google Scholar 

  11. Kunttu, I., Lepistö, L., Rauhamaa, J., Visa, A.: Multiscale Fourier descriptor for shape classification. In: Proceedings of 12th International Conference on Image Analysis and Processing, pp. 536–541 (2003)

  12. Kunttu, I., Lepistö, L., Rauhamaa, J., Visa, A.: Multiscale Fourier descriptor for shape-based image retrieval. In: Proceedings of 17th International Conference of Pattern Recognition, vol. 2, pp. 765–768 (2004)

  13. Mehtre B.M., Kankanhalli M.S., Lee W.F. (1997). Shape measures for content based image retrieval: a comparison. Inf. Process. Manage. 33(3): 319–337

    Article  Google Scholar 

  14. Mehtre B.M., Kankanhalli M.S., Lee W.F. (1998). Content-based image retrieval using a composite color-shape approach. Inf. Process. Manage. 34(1): 109–120

    Article  Google Scholar 

  15. Rauhamaa, J., Reinius, R.: Paper web imaging with advanced defect classification. In: Proceedings of the 2002 TAPPI Technology Summit (2002)

  16. Smeulders A.W.M., Worring M., Santini S., Gupta A., Jain R. (2000). Content-based image retrieval at the end of the early years. IEEE Trans Pattern Anal. Mach. Intell. 22(12): 1349–1380

    Article  Google Scholar 

  17. Zhang, D.S., Lu, G.: A comparative study of fourier descriptors with for shape representation and retrieval. In: Proceedings of the 5th Asian Conference on Computer Vision, pp. 646–651 (2002)

  18. Zhang D.S., Lu G. (2003a). A comparative study of curvature scale space and Fourier descriptors for shape-based image retrieval. J. Vis. Commun. Image Representation 14(1): 41–60

    Google Scholar 

  19. Zhang D.S., Lu G. (2003). Evaluation of MPEG-7 shape descriptors against other shape descriptors. Multimedia Sys. 9(1): 15–30

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Tampere University of Technology, Institute of Signal Processing, P.O. Box 553, 33101, Tampere, Finland

    Iivari Kunttu, Leena Lepistö & Ari Visa

  2. Process Industry, ABB Oy, P.O. Box 94, 00381, Helsinki, Finland

    Juhani Rauhamaa

Authors
  1. Iivari Kunttu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Leena Lepistö
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Juhani Rauhamaa
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ari Visa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Iivari Kunttu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kunttu, I., Lepistö, L., Rauhamaa, J. et al. Fourier-Based Object Description in Defect Image Retrieval. Machine Vision and Applications 17, 211–218 (2006). https://doi.org/10.1007/s00138-006-0030-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-006-0030-6

Keywords

Search

Navigation