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.
Original language | English (US) |
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Pages (from-to) | 123-126 |
Number of pages | 4 |
Journal | Proceedings of SPIE - The International Society for Optical Engineering |
Volume | 2568 |
DOIs | |
State | Published - Aug 11 1995 |
Externally published | Yes |
Event | Neural, Morphological, and Stochastic Methods in Image and Signal Processing 1995 - San Diego, United States Duration: Jul 9 1995 → Jul 14 1995 |
Keywords
- 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