Optimal morphological peak classification

Edward R. Dougherty, Yidong Chen

Research output: Contribution to journalConference articlepeer-review


The morphological top-hat transform is often used to locate bright peaks in a gray-scale image. The method can be problematic when there are two classes of peaks, one corresponding to valid objects and the other to noise. The present paper employs Bayesian estimation in conjunction with a multinomial distribution corresponding to levels of peak heights in the top-hat image to arrive at an optimal conditional-expectation estimator for the number of images in a random sample of images that contain a given number of valid peaks.

Original languageEnglish (US)
Pages (from-to)123-126
Number of pages4
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - Aug 11 1995
Externally publishedYes
EventNeural, Morphological, and Stochastic Methods in Image and Signal Processing 1995 - San Diego, United States
Duration: Jul 9 1995Jul 14 1995


  • Bayesian Estimation
  • Morphology
  • Top-hat transform

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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