Leather inspection through singularities detection using wavelet transforms

  • A. Branca
  • M. G. Abbate
  • F. P. Lovergine
  • G. Attolico
  • A. Distante
Poster Session D: Biomedical Applications, Detection, Control & Surveillance, Inspection, Optical Character Recognition
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1311)

Abstract

The major problem in leather inspection is to separate defects from the background exhibiting a wide range of visual appearances. Leather defects, characterized by oriented structures, cannot be easily discriminated from the structures typical of the normal surface. Though gaussian filters generally represent a successfull tool to smooth out the structures on the background, a wrong choice of the resolution can preclude the detection of defective regions (singularities) in the subsequent analysis. However, wavelet transforms can be profitably used for studying the evolution of singularities across different scales. Suitable kernels for this transform does allow multiscale singularities analysis through the detection of local maxima in wavelet transform modulus.

Keywords

Gradient Vector Edge Point Modulus Maximum Local Regularity Defective Region 
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.
Download to read the full conference paper text

References

  1. 1.
    Mallat, S. and Hwang, W.L. “Singularity Detection and Processing with Wavelets”, Technical Report March 1991.Google Scholar
  2. 2.
    A.Rao A Taxonomy for Texture Description and Identification. Springer-Verlag.Google Scholar
  3. 3.
    R.Rao and B.G.Schunk Computing oriented texture fields CVGIP: Graphical Models Image Processing 53 (1991) 157–185.CrossRefGoogle Scholar
  4. 4.
    C.Boumann and B.Liu “Multiple resolution segmentation of textured images” IEEE Trans. PAMI Vol 13 No 2 (February 1991) pp 99–113.Google Scholar
  5. 5.
    R.Muzzolini, Y-HYang and R.Pierson “Multiresolution texture segmentation with application to diagnostic ultrasound images” IEEE Trans. Med. Imag. VOl 12 No 1 (March 1993) p 108–123.CrossRefGoogle Scholar
  6. 6.
    M.Unser and M.Eden “Multiresolution feature extraction and selection for texture segmentation” IEEE Tras. PAMI Vol 11 No 7 (July 1989) pp 717–728.Google Scholar
  7. 7.
    A.Sher and A.Rosenfeld “Detecting and Extracting compact textured objects using pyramids”, Image F9 Vision Comput. Vol 7 No 3 (August 1989) pp129–134.CrossRefGoogle Scholar
  8. 8.
    J.Spaniol, M.R.Belmont, I.R.Summers, W.Vennart “Generalized Multiresolution analysis of magnetic resonance images” Image a nd Vision Computing VOl 12 No 9 (November 1994).Google Scholar
  9. 9.
    A.Meisels, R.Versano “Token-textured Object Detection by Pyramid” Image and Vision Computing Vol 10 No 1 (February 1992).Google Scholar
  10. 10.
    W.Wen, R.J.Fryer “Multiscale texture element slection” Pattern Recognition Letters Vol 12 No12 (December 1991).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • A. Branca
    • 1
  • M. G. Abbate
    • 1
  • F. P. Lovergine
    • 1
  • G. Attolico
    • 1
  • A. Distante
    • 1
  1. 1.Istituto Elaborazione Segnali ed Immagini - C.N.R.BariItaly

Personalised recommendations