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Image Preprocessing Assessment Detecting Low Contrast Regions under Non-homogeneous Light Conditions

  • Camilo Vargas
  • Jeyson Molina
  • John W. Branch
  • Alejandro Restrepo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8334)

Abstract

This paper focuses on evaluating the pre-processing impact in detecting low contrast regions on irregular surfaces with non-homogeneous lighting. Non homogeneous lighting represents an obstacle to the correct segmentation and subsequent classification of relevant image regions. For example in grayscale images, intensity variations are detected on the same region. Therefore lower contrast regions require an adequate sensitivity level at the segmentation stage. Segmentation, description and classification techniques will be applied over a set of images without pre-processing and over the same set of images with pre-processing, in order to achieve the assessment. The images used in this paper were obtained from a visual inspection prototype for flaw detection on dentures. The outcome shows that an appropriate image pre-processing is required to improve the detection process performance for the given circumstances.

Keywords

Flaw detection automated visual inspection low contrast nonhomogeneous light 

References

  1. 1.
    Rodríguez, J.C., Molina, J., Atencio, P., Branch, J.W., Alejandro, R.: Anisotropic filtering assessment applied on superficial defects enhancement under non homogenous light conditions. Revista Avances en Sistemas e Informática 8(3), 57–62 (2011)Google Scholar
  2. 2.
    Kim, S., Allebach, J.P.: Optimal unsharp mask for image sharpening and noise removal. J. Electron. Imaging. 14(2) (2005)Google Scholar
  3. 3.
    Mahmood, N.H., Razif, M.R.M.: MTAN Gany Comparison between Median, Unsharp and Wiener filter and its effect on ultrasound stomach tissue image segmentation for Pyloric Stenosis. International Journal of Applied Science and Technology 1(5), 218–226 (2011)Google Scholar
  4. 4.
    Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena 60(1-4), 259–268 (1992)CrossRefzbMATHGoogle Scholar
  5. 5.
    Chambolle, A.: An Algorithm for Total Variation Minimization and Applications. Journal of Mathematical Imaging and Vision 20(1-2), 89–97 (2004)MathSciNetGoogle Scholar
  6. 6.
    Wold, S., Esbensen, K., Geladi, P.: Principal Component Analysis. Chemometrics and Intelligent Laboratory Systems 2(1), 37–52 (1987)CrossRefGoogle Scholar
  7. 7.
    Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research 16, 321–357 (2002)zbMATHGoogle Scholar
  8. 8.
    Cortes, C., Vapnik, V.: Support-Vector Networks. Machine Leaming 20, 273–297 (1995)zbMATHGoogle Scholar
  9. 9.
    Mery, D.: Crossing Line Profile: A New Approach to Detecting Defects in Aluminium Die Castings. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 725–732. Springer, Heidelberg (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Camilo Vargas
    • 1
  • Jeyson Molina
    • 1
  • John W. Branch
    • 1
  • Alejandro Restrepo
    • 2
  1. 1.Escuela de Sistemas, Facultad de MinasUniversidad Nacional de Colombia Sede MedellínColombia
  2. 2.Instituto Tecnológico MetropolitanoMedellínColombia

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