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)


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.


image segmentation local thresholding spot detection petroleum geology application 


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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

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