Optimal morphological peak classification

Edward R. Dougherty; Yidong Chen

doi:10.1117/12.216345

11 August 1995 Optimal morphological peak classification

Edward R. Dougherty, Yidong Chen

Proceedings Volume 2568, Neural, Morphological, and Stochastic Methods in Image and Signal Processing; (1995) https://doi.org/10.1117/12.216345
Event: SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, 1995, San Diego, CA, United States

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.

Citation Download Citation

Edward R. Dougherty and Yidong Chen "Optimal morphological peak classification", Proc. SPIE 2568, Neural, Morphological, and Stochastic Methods in Image and Signal Processing, (11 August 1995); https://doi.org/10.1117/12.216345

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