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Robust Gray-Level Histogram Gaussian Characterisation

  • José Manuel Iñesta
  • Jorge Calera-Rubio
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2396)

Abstract

One of the most utilised criteria for segmenting an image is the gray level values of the pixels in it. The information for identifying similar gray values is usually extracted from the image histogram. We have analysed the problems that may arise when the histogram is automatically characterised in terms of multiple Gaussian distributions and solutions have been proposed for special situations that we have named degenerated modes. The convergence of the method is based in the expectation maximisation algorithm and its performance has been tested on images from different application fields like medical imaging, robotic vision and quality control.

Keywords

Image Segmentation Grey Level Expectation Maximisation Algorithm Image Histogram Medical Image Analysis 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • José Manuel Iñesta
    • 1
  • Jorge Calera-Rubio
    • 1
  1. 1.Departamento de Lenguajes y Sistemas InformáticosUniversidad de AlicanteSpain

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