Advertisement

Spot Detection in Images with Noisy Background

  • Denis Ferraretti
  • Luca Casarotti
  • Giacomo Gamberoni
  • Evelina Lamma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6978)

Abstract

One of the most recurrent problem in digital image processing applications is segmentation. Segmentation is the separation of components in the image: the ability to identify and to separate objects from the background. Depending on the application, this activity can be very difficult and segmentation accuracy is crucial in order to obtain reliable results. In this paper we propose an approach for spot detection in images with noisy background. The overall approach can be divided in three main steps: image segmentation, region labeling and selection. Three segmentation algorithms, based on global or local thresholding technique, are developed and tested in a real-world petroleum geology industrial application. To assess algorithm accuracy we use a simple voting technique: by a visual comparison of the results, three domain experts vote for the best algorithms. Results are encouraging, in terms of accuracy and time reduction, especially for the algorithm based on local thresholding technique.

Keywords

image segmentation local thresholding spot detection petroleum geology application 

References

  1. 1.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn., pp. 689–794. Prentice-Hall, Englewood Cliffs (2008)Google Scholar
  2. 2.
    Shapiro, L.G., Stockman, G.C.: Computer Vision, pp. 279–325. Prentice-Hall, Englewood Cliffs (2001)Google Scholar
  3. 3.
    Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: Ideas, influences, and trends of the new age. ACM Comput. Surv. 40(2), Article 5 (2008)Google Scholar
  4. 4.
    Comaniciu, D., Meer, P.: Mean Shift: A Robust Approach toward Feature Space Analysis. IEEE Trans. Pattern Analysis Machine Intell. 24(5), 603–619 (2002)CrossRefGoogle Scholar
  5. 5.
    Wang, J.Z., Li, J., Gray, R.M., Wiederhold, G.: Unsupervised Multiresolution Segmentation for Images with Low Depth of Field. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(1), 85–90 (2001)CrossRefGoogle Scholar
  6. 6.
    Tu, Z., Zhu, S.: Image Segmentation by Data-Driven Markov Chain Monte Carlo. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(5) (May 2002)Google Scholar
  7. 7.
    Carson, C., Belongie, S., Greenspan, H., Malik, J.: Blobworld: Image segmentation using expectation-maximization and its application to image querying. IEEE Trans. Pattern Analysis and Machine Intelligence 24(8), 1026–1038 (2002)CrossRefGoogle Scholar
  8. 8.
    Chen, J., Pappas, T., Mojsilovic, A., Rogowitz, B.: Adaptive image segmentation based on color and texture. In: Proceedings of the IEEE International Conference on Image Processing, ICIP (2002)Google Scholar
  9. 9.
    Deng, Y., Majunath, B.: Unsupervised segmentation of color-texture regions in images and video. IEEE Trans. Pattern Anal. Mach. Intell. 23(8), 800–810 (2001)CrossRefGoogle Scholar
  10. 10.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Systems, Man, and Cybernetics 9(1), 62–66 (1979)CrossRefGoogle Scholar
  11. 11.
    Niblack, W.: An Introduction to Digital Image Processing. Prentice-Hall, Englewood Cliffs (1986)Google Scholar
  12. 12.
    Casarotti, L.: Algoritmi avanzati di analisi delle immagini da pozzi petroliferi (Advanced algorithm for borehole image processing). Master’s Thesis, University of Ferrara (2011)Google Scholar
  13. 13.
    Ferraretti, D.: Analisi di immagini da pozzi petroliferi e loro classificazione (Borehole image analysis and classification). Master’s Thesis, University of Ferrara (2006)Google Scholar
  14. 14.
    Abramoff, M.D., Magelhaes, P.J., Ram, S.J.: Image Processing with Image. J. Biophotonics International 11(7), 36–42 (2004)Google Scholar
  15. 15.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)CrossRefGoogle Scholar
  16. 16.
    Serra, J.: Image Analysis and Mathematical Morphology. Academic Press, London (1982)zbMATHGoogle Scholar
  17. 17.
    Mancas, M., Gosselin, B., Benoît, M.: Segmentation using a region-growing thresholding. Proceedings of the SPIE 5672, 12–13 (2005)CrossRefGoogle Scholar
  18. 18.
    Ferraretti, D., Gamberoni, G., Lamma, E., Di Cuia, R., Turolla, C.: An AI Tool for the Petroleum Industry Based on Image Analysis and Hierarchical Clustering. In: Corchado, E., Yin, H. (eds.) IDEAL 2009. LNCS, vol. 5788, pp. 276–283. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Denis Ferraretti
    • 1
  • Luca Casarotti
    • 1
  • Giacomo Gamberoni
    • 2
  • Evelina Lamma
    • 1
  1. 1.ENDIF-Dipartimento di IngegneriaUniversità di FerraraFerraraItaly
  2. 2.intelliWARE sncFerraraItaly

Personalised recommendations