Bimodal histogram transformation based on maximum likelihood parameter estimates in univariate Gaussian mixtures

  • Nette Schultz
  • Jens Michael Carstensen
Poster Session D: Biomedical Applications, Detection, Control & Surveillance, Inspection, Optical Character Recognition
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1311)


This paper presents a bimodal histogram transformation procedure where conjugate gradient optimization is used for estimating maximum likelihood parameters of univariate Gaussian mixtures. The paper only deals with bimodal distributions but extension to multimodal distributions is fairly straightforward. The transformation is suggested as a preprocessing step that provides a standardized input to e.g. a classifier. This approach is used for pixelwise classification in RGB-images of meat.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Nette Schultz
    • 1
  • Jens Michael Carstensen
    • 1
  1. 1.Department of Mathematical ModellingTechnical University of DenmarkLyngbyDenmark

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