Optimal morphological peak classification

Edward R. Dougherty; Yidong Chen

doi:10.1117/12.216345

Optimal morphological peak classification

Edward R. Dougherty, Yidong Chen

Producción científica: Conference article › revisión exhaustiva

Resumen

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.

Idioma original	English (US)
Páginas (desde-hasta)	123-126
Número de páginas	4
Publicación	Proceedings of SPIE - The International Society for Optical Engineering
Volumen	2568
DOI	https://doi.org/10.1117/12.216345
Estado	Published - ago 11 1995
Publicado de forma externa	Sí
Evento	Neural, Morphological, and Stochastic Methods in Image and Signal Processing 1995 - San Diego, United States Duración: jul 9 1995 → jul 14 1995

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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title = "Optimal morphological peak classification",

abstract = "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.",

keywords = "Bayesian Estimation, Morphology, Top-hat transform",

author = "Dougherty, {Edward R.} and Yidong Chen",

note = "Publisher Copyright: {\textcopyright} 1995 SPIE.; Neural, Morphological, and Stochastic Methods in Image and Signal Processing 1995 ; Conference date: 09-07-1995 Through 14-07-1995",

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AU - Dougherty, Edward R.

AU - Chen, Yidong

PY - 1995/8/11

Y1 - 1995/8/11

N2 - 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.

AB - 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.

KW - Bayesian Estimation

KW - Morphology

KW - Top-hat transform

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DO - 10.1117/12.216345

M3 - Conference article

AN - SCOPUS:85079278135

SN - 0277-786X

VL - 2568

SP - 123

EP - 126

JO - Proceedings of SPIE - The International Society for Optical Engineering

JF - Proceedings of SPIE - The International Society for Optical Engineering

T2 - Neural, Morphological, and Stochastic Methods in Image and Signal Processing 1995

Y2 - 9 July 1995 through 14 July 1995

ER -

Optimal morphological peak classification

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